Systems and methods for inferring a time zone of a node profile using electronic activities

ABSTRACT

The present disclosure relates to inferring a time zone of a node profile using electronic activities. A method can include accessing a plurality of electronic activities transmitted or received via a plurality of electronic accounts. The method can include identifying, for a node profile, a set of electronic activities sent from or received by an electronic account of the plurality of electronic accounts linked to the node profile within a time period. The method can include identifying, for each electronic activity of the set of electronic activities, a timestamp at which the electronic activity was sent or received. The method can include generating, for each of a plurality of time intervals within the time period, a temporal distribution of electronic activity based on respective timestamps of each electronic activity. The method can include determining a time zone of the node profile based on the temporal distribution.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of and priority to U.S.Provisional Patent Application 62/747,452, filed Oct. 18, 2018, U.S.Provisional Patent Application 62/725,999, filed Aug. 31, 2018, and U.S.Provisional Patent Application 62/676,187, filed May 24, 2018, each ofwhich are incorporated herein by reference for all purposes.

BACKGROUND

An organization may attempt to manage or maintain a system of recordassociated with electronic communications at the organization. Thesystem of record can include information such as contact information,logs, and other data associated with the electronic activities. Dataregarding the electronic communications can be transmitted betweencomputing devices associated with one or more organizations using one ormore transmission protocols, channels, or formats, and can containvarious types of information. For example, the electronic communicationcan include information about a sender of the electronic communication,a recipient of the electronic communication, and content of theelectronic communication. The information regarding the electroniccommunication can be input into a record being managed or maintained bythe organization. However, due to the large volume of heterogeneouselectronic communications transmitted between devices and the challengesof manually entering data, inputting the information regarding eachelectronic communication into a system of record can be challenging,time consuming, and error prone.

SUMMARY

At least one aspect of this disclosure is directed to a method forinferring a time zone of a node profile using electronic activities. Themethod can include accessing, by one or more processors, a plurality ofelectronic activities transmitted or received via a plurality ofelectronic accounts associated with a data source provider. The methodcan include identifying, by the one or more processors, for a nodeprofile maintained by the one or more processors, a set of electronicactivities sent from or received by an electronic account of theplurality of electronic accounts linked to the node profile within atime period. The method can include identifying, by the one or moreprocessors, for each electronic activity of the set of electronicactivities, a timestamp at which the electronic activity was sent orreceived. The method can include generating, by the one or moreprocessors, for each of a plurality of time intervals within the timeperiod, a temporal distribution of electronic activity for the timeinterval based on respective timestamps of each electronic activity ofthe set of electronic activities within the time interval. The methodcan include determining, by the one or more processors, a time zone ofthe node profile based on the temporal distribution of electronicactivity. The method can include storing, by the one or more processorsin one or more data structures, an association between the determinedtime zone and the node profile.

In some implementations, the method can include receiving, by the one ormore processors, an electronic activity not included in the set ofelectronic activities. In some implementations, the method can includeparsing, by the one or more processors, the electronic activity toidentify a participant and a timestamp. In some implementations, themethod can include determining, by the one or more processors, aplurality of candidate node profiles, based on the participant. In someimplementations, the method can include selecting, by the one or moreprocessors, one of the plurality of candidate node profiles to matchwith the electronic activity based on a time zone of selected candidatenode profile and the timestamp of the electronic activity.

In some implementations, the temporal distribution can be a firsttemporal distribution. In some implementations, determining the timezone of the node profile can include identifying, by the one or moreprocessors, a plurality of second temporal distributions each associatedwith a respective predetermined time zone. In some implementations, themethod can include selecting, by the one or more processors, one of theplurality of second temporal distributions based on a comparison withthe first temporal distribution. In some implementations, the method caninclude determining, by the one or more processors, the time zone basedon the respective predetermined time zone of the selected secondtemporal distribution.

In some implementations, each second temporal distribution can begenerated based on a respective second set of electronic activities ofone or more second node profiles. In some implementations, each secondnode profile can be associated with the respective predetermined timezone.

In some implementations, the method can include identifying, by the oneor more processors, a system of record having at least one record objectassociated with the node profile. In some implementations, the methodcan include transmitting, by the one or more processors, an instructionto cause the system of record to update a time zone field-value pair ofthe at least one record object corresponding to the node profile.

In some implementations, the method can include updating, by the one ormore processors, a time zone field-value pair of the node profile tomatch the determined time zone.

In some implementations, the method can include generating, by the oneor more processors, a notification for a user associated with the nodeprofile. In some implementations, the method can include scheduling, bythe one or more processors, transmission of the notification to acomputing device of the user associated with the node profile based onthe determined time zone. In some implementations, schedulingtransmission of the notification can include identifying, by the one ormore processors, a restricted notification period based on thedetermined time zone. In some implementations, the method can includerestricting, by the one or more processors, transmission of thenotification to the computing device of the user associated with thenode profile during the restricted notification period.

In some implementations, the method can include parsing, by the one ormore processors, a first electronic activity of the set of electronicactivities to identify a character string corresponding to a geographiclocation. In some implementations, the method can include determining,by the one or more processors, the time zone based on the geographiclocation. In some implementations, parsing the first electronic activityof the set of electronic activities to identify the character stringcorresponding to the geographic location can include extracting, by theone or more processors, the geographic location from a body of the firstelectronic activity.

In some implementations, identifying the set of electronic activitiescan include selecting each electronic activity to include in the set ofelectronic activities responsive to determining that the electronicactivity is of a first electronic activity type.

In some implementations, the method can include comparing, by the one ormore processors, the time zone with a value of a field-value pair of thenode profile corresponding to a location. In some implementations, themethod can include updating, by the one or more processors, a confidencescore of the value of the field-value pair of the node profilecorresponding to the location, based on the comparison.

In some implementations, determining the time zone of the node profilebased on the temporal distribution of electronic activity can furtherinclude determining, by the one or more processors, that the temporaldistribution of electronic activity corresponds to the time zone of thenode profile using a machine learning model trained to classify temporaldistributions of electronic activity.

At least another aspect of this disclosure is directed to a system forinferring a time zone of a node profile using electronic activities. Thesystem can include one or more hardware processors configured bymachine-readable instructions. The one or more hardware processors canbe configured to access a plurality of electronic activities transmittedor received via a plurality of electronic accounts associated with adata source provider. The one or more hardware processors can beconfigured to identify, for a node profile maintained by the one or moreprocessors, a set of electronic activities sent from or received by anelectronic account of the plurality of electronic accounts linked to thenode profile within a time period. The one or more hardware processorscan be configured to identify, for each electronic activity of the setof electronic activities, a timestamp at which the electronic activitywas sent or received. The one or more hardware processors can beconfigured to generate, for each of a plurality of time intervals withinthe time period, a temporal distribution of electronic activity for thetime interval based on respective timestamps of each electronic activityof the set of electronic activities within the time interval. The one ormore hardware processors can be configured to determine a time zone ofthe node profile based on the temporal distribution of electronicactivity. The one or more hardware processors can be configured tostore, in one or more data structures, an association between thedetermined time zone and the node profile.

In some implementations, the one or more hardware processors can befurther configured to receive an electronic activity not included in theset of electronic activities. In some implementations, the one or morehardware processors can be further configured to parse the electronicactivity to identify a participant and a timestamp. In someimplementations, the one or more hardware processors can be furtherconfigured to determine a plurality of candidate node profiles, based onthe participant. In some implementations, the one or more hardwareprocessors can be further configured to select one of the plurality ofcandidate node profiles to match with the electronic activity based on atime zone of selected candidate node profile and the timestamp of theelectronic activity.

In some implementations, the temporal distribution can be a firsttemporal distribution. In some implementations, the one or more hardwareprocessors can be further configured to determine the time zone of thenode profile by identifying a plurality of second temporal distributionseach associated with a respective predetermined time zone. In someimplementations, the one or more hardware processors can be furtherconfigured to select one of the plurality of second temporaldistributions based on a comparison with the first temporaldistribution. In some implementations, the one or more hardwareprocessors can be further configured to determine the time zone based onthe respective predetermined time zone of the selected second temporaldistribution. In some implementations, the one or more hardwareprocessors are further configured by the machine readable instructionsto generate each second temporal distribution based on a respectivesecond set of electronic activities of one or more second node profiles.In some implementations, each second node profile can be associated withthe respective predetermined time zone.

In some implementations, the one or more hardware processors can befurther configured to identify a system of record having at least onerecord object associated with the node profile. In some implementations,the one or more hardware processors can be further configured totransmit an instruction to cause the system of record to update a timezone field-value pair of the at least one record object corresponding tothe node profile.

In some implementations, the one or more hardware processors are furtherconfigured by the machine readable instructions to update a time zonefield-value pair of the node profile to match the determined time zone.

In some implementations, the one or more hardware processors can befurther configured to generate a notification for a user associated withthe node profile. In some implementations, the one or more hardwareprocessors can be further configured to schedule transmission of thenotification to a computing device of the user associated with the nodeprofile based on the determined time zone.

In some implementations, the one or more hardware processors are furtherconfigured by the machine readable instructions to schedule transmissionof the notification by identifying, by the one or more processors, arestricted notification period based on the determined time zone. Insome implementations, the one or more hardware processors can be furtherconfigured to restrict transmission of the notification to the computingdevice of the user associated with the node profile during therestricted notification period.

At least another aspect of this disclosure is directed to anon-transient computer-readable storage medium having instructionsembodied thereon. The instructions can be executable by one or moreprocessors to perform a method for inferring a time zone of a nodeprofile using electronic activities. The method can include accessing aplurality of electronic activities transmitted or received via aplurality of electronic accounts associated with a data source provider.The method can include identifying, for a node profile maintained by theone or more processors, a set of electronic activities sent from orreceived by an electronic account of the plurality of electronicaccounts linked to the node profile within a time period. The method caninclude identifying, for each electronic activity of the set ofelectronic activities, a timestamp at which the electronic activity wassent or received. The method can include generating, for each of aplurality of time intervals within the time period, a temporaldistribution of electronic activity for the time interval based onrespective timestamps of each electronic activity of the set ofelectronic activities within the time interval. The method can includedetermining a time zone of the node profile based on the temporaldistribution of electronic activity. The method can include storing, inone or more data structures, an association between the determined timezone and the node profile.

BRIEF DESCRIPTIONS OF THE DRAWINGS

FIG. 1 illustrates a tiered system architecture for aggregatingelectronic activities and synchronizing the electronic activities to oneor more systems of record according to embodiments of the presentdisclosure;

FIG. 2 illustrates a process flow for aggregating electronic activitiesand synchronizing the electronic activities to one or more systems ofrecord according to embodiments of the present disclosure;

FIG. 3 illustrates a processing flow diagram for aggregating electronicactivities and synchronizing the electronic activities to one or moresystems of record according to embodiments of the present disclosure;

FIG. 4 illustrates a node graph generation system for constructing anode graph based on electronic activity according to embodiments of thepresent disclosure;

FIGS. 5A-5C illustrate various types of example electronic activitiesaccording to embodiments of the present disclosure;

FIG. 6A illustrates a representation of a node profile of a nodeaccording to embodiments of the present disclosure;

FIG. 6B illustrates representations of three electronic activities andrepresentations of three states of a node profile of a node according toembodiments of the present disclosure;

FIG. 7 illustrates a series of electronic activities between two nodesaccording to embodiments of the present disclosure;

FIG. 8 illustrates electronic activities involving two nodes and theimpact a time decaying score has on the connection strength between thetwo nodes according to embodiments of the present disclosure;

FIG. 9 illustrates a block diagram of an example electronic activitylinking engine according to embodiments of the present disclosure;

FIG. 10 illustrates a plurality of example record objects, and theirinterconnections, according to embodiments of the present disclosure;

FIG. 11 illustrates the restriction of a first grouping of recordobjects with a second grouping of record objects according toembodiments of the present disclosure;

FIG. 12 illustrates the application of a plurality of matchingstrategies and then pruning of the matched record objects with a secondplurality of matching strategies according to embodiments of the presentdisclosure;

FIG. 13 illustrates an example calculation for calculating theengagement score of an opportunity record object according toembodiments of the present disclosure;

FIG. 14 illustrates an example user interface identifying various piecesof information that can be extracted from an electronic activityaccording to embodiments of the present disclosure;

FIG. 15 illustrates an example user interface identifying a recordobject corresponding to an opportunity according to embodiments of thepresent disclosure;

FIG. 16 illustrates a block diagram of an example process flow forprocessing electronic activities in a single-tenant configurationaccording to embodiments of the present disclosure;

FIG. 17 illustrates a block diagram of an example process flow forprocessing electronic activities in a multi-tenant configurationaccording to embodiments of the present disclosure;

FIG. 18 illustrates a block diagram of an example process flow formatching electronic activities with record objects according toembodiments of the present disclosure;

FIG. 19 illustrates a block diagram of an example method to matchelectronic activities directly to record objects according toembodiments of the present disclosure;

FIG. 20 illustrates a block diagram of an example process flow formatching electronic activities with record objects according toembodiments of the present disclosure;

FIG. 21 illustrates a block diagram of an example method to matchelectronic activities with record objects according to embodiments ofthe present disclosure;

FIG. 22 illustrates a block diagram of an example process to matchelectronic activities with node profiles according to embodiments of thepresent disclosure;

FIG. 23 illustrates a block diagram of an example method to matchelectronic activities with node profiles according to embodiments of thepresent disclosure;

FIG. 24 illustrates a block diagram of an example method to matchelectronic objects with node profiles according to embodiments of thepresent disclosure; and

FIG. 25 illustrates a block diagram of a series of electronic activitiesbetween two nodes according to embodiments of the present disclosure;

FIG. 26 illustrates a representation of a node profile of a nodeaccording to embodiments of the present disclosure;

FIG. 27 illustrates a block diagram of an example method to generateconfidence scores of values of fields based on data points according toembodiments of the present disclosure;

FIG. 28 illustrates a use case diagram of an example system forinferring a time zone of a node profile using electronic activities;

FIG. 29 illustrates an example graph showing a plurality of graphicalrepresentations of temporal distributions of electronic activity atvarious time intervals;

FIG. 30 illustrates a flow diagram of an example method for inferring atime zone of a node profile using electronic activities; and

FIG. 31 illustrates a simplified block diagram of a representativeserver system and client computer system according to embodiments of thepresent disclosure.

DETAILED DESCRIPTION

The present disclosure relates to systems and methods for constructing anode graph based on electronic activity. The node graph can include aplurality of nodes and a plurality of edges between the nodes indicatingactivity or relationships that are derived from a plurality of datasources that can include one or more types of electronic activities. Theplurality of data sources can include email or messaging servers, phoneservers, servers storing calendar information, meeting information,among others. The plurality of data sources further includes systems ofrecord, such as customer relationship management systems, enterpriseresource planning systems, document management systems, applicanttracking systems or other source of data that may maintain electronicactivities, activities or records.

The present disclosure further relates to systems and methods for usingthe node graph to manage, maintain, improve, or otherwise modify one ormore systems of record by linking and or synchronizing electronicactivities to one or more record objects of the systems of record. Inparticular, the systems described herein can be configured toautomatically synchronize real-time or near real-time electronicactivity to one or more objects of systems of record. The systems canfurther extract business process information from the systems of recordand in combination with the node graph, use the extracted businessprocess information to improve business processes and to provide datadriven solutions to improve such business processes.

The present disclosure relates to systems and methods for constructing anode graph based on electronic activity. The node graph can include aplurality of nodes and a plurality of edges between the nodes indicatingactivity or relationships that are derived from a plurality of datasources that can include one or more types of electronic activities. Thepresent disclosure further relates to systems and methods for using thenode graph to manage, maintain, improve, or otherwise modify one or moresystems of record by linking and or synchronizing electronic activitiesto one or more record objects of the systems of record. In particular,the systems described herein can be configured to automaticallysynchronize real-time or near real-time electronic activity to one ormore objects of systems of record. The systems can further extractbusiness process information from the systems of record and incombination with the node graph, use the extracted business processinformation to improve business processes and to provide data drivensolutions to improve such business processes.

At least one aspect of the present disclosure is directed to systems andmethods for maintaining an electronic activity derived member nodenetwork. For example, a node profile for a member node in a node graphcan include information such as first name, last name, company, and jobtitle. However, it may be challenging to accurately and efficientlypopulate fields in a node profile due to large number of member nodes.Furthermore, permitting self-population of node profiles by member nodescan result in erroneous data values, improper data values, or otherwiseundesired data values due in part to human bias. Having erroneous datavalues in a node profile can cause downstream components or functionsthat perform processing using the node profiles to malfunction orgenerate faulty outputs.

Thus, systems and methods of the present technical solution can generatean electronic activity derived member node network that includes nodeprofiles for a member node that is generated based on electronicactivity. By generating the member node profile for the member nodeusing electronic activity and a statistical analysis, the system cangenerate the profile with data fields and values that pass averification process or statistical analysis using electronicactivities.

Referring briefly to FIG. 1, FIG. 1 illustrates a tiered systemarchitecture for aggregating electronic activities and synchronizing theelectronic activities to one or more systems of record according toembodiments of the present disclosure. As shown in FIG. 1, at the firsttier, the system, such as the data processing system 9300 (shown in FIG.3), aggregates electronic activities from one or more data sourceproviders. At the second tier, the system extracts information from theaggregated electronic activities and one or more systems of record ofone or more data source providers to construct and maintain a node graphincluding the plurality of nodes and edges indicating the connectionsbetween the nodes. At the third tier, the system utilizes the electronicactivities, the systems of record, and the node graph to provide datadriven insights to improve one or more business processes of the datasource providers and to assist various data source providers inextracting data driven insights.

FIG. 2 illustrates a process flow for aggregating electronic activitiesand synchronizing the electronic activities to one or more systems ofrecord according to embodiments of the present disclosure. The systemcan be configured to receive and aggregate electronic activitiesidentifying one or more nodes. The system can parse the electronicactivities and extract information from the electronic activities togenerate node profiles for each node, log activities and maintainchanges made to each of the node profiles maintained by the system. Thesystem can further be configured to extract information from theelectronic activities of the nodes and determine insights or metricsthat can be shared with one or more other nodes and the users of thesystem. The system can be further configured to synchronize theelectronic activities to objects of one or more systems of record.

In a particular use case, sales representatives of an organization maybe involved in electronic activities, such as emails, phone calls,meetings, among others that can be tracked and captured by the systemvia ingestion from one or more data sources of the organization or otherorganizations. The system can extract information from the electronicactivities that may be associated with deals or opportunities the salesrepresentatives are working on. The system can use the information fromthese electronic activities to generate reports for managers of theorganization. These reports are generated based on data derived fromelectronic activity without requiring the sales representatives toperform any additional activities. Furthermore, the managers also do notneed to spend time generating these reports as the system canautomatically generate these reports. Furthermore, the system canidentify trends and behaviors that may be determined through machinelearning techniques otherwise not tracked by the managers, therebyproviding reports that may otherwise not be generated by the managers.Further, sales representatives may also no longer be required to spendtime synchronizing electronic activities to one or more systems ofrecord. Rather, the system can be configured to automaticallysynchronize the electronic activities to the appropriate objects of theone or more systems of record. The system can further receiveinformation from the one or more systems and records to determine theresults associated with the sales representative's efforts and performanalytics to generate recommendations to assist the salesrepresentatives achieve their goals and eventually improve theirperformance as sales representatives as well as provide companymanagement with recommendations about improving the performance of theoverall business.

Referring now to FIG. 3, FIG. 3 illustrates a processing flow diagramfor aggregating electronic activities, processing the electronicactivities to update node profiles of people and to construct a nodegraph, and synchronizing the electronic activities to one or moresystems of record. The process flow 9302 can be executed by a dataprocessing system 9300 that can receive electronic activity and otherdata from a plurality of data source providers 9350(1)-9350(N). Eachdata source provider 9350 can include one or more data sources 9355 a-nand/or one or more system of record instances 9360. Examples of datasources can include electronic mail servers, telephone log servers,contact servers, other types of servers and end-user applications thatmay receive or maintain electronic activity data or profile datarelating to one or more nodes. The data processing system 9300 caningest electronic activity (9307). The data processing system 9300 canfeaturize (9310) and tag the ingested electronic activity (9307) andstore the featurized data in a featurized data store (9315). The dataprocessing system 9300 can process the featurized data (9320) togenerate a node graph 9325 including a plurality of node profiles. Thedata processing system 9300 can further maintain a plurality of systemof record instances 9330(1)-9330(N) corresponding to system of recordinstances of the data source providers 9350. The data processing system9300 can utilize the system of record instances to augment the nodeprofiles of the node graph by synchronizing data stored in the system ofrecord instances maintained by the data processing system (9300). Thedata processing system 9300 can further match (9340) the ingestedelectronic activities to one or more record objects maintained in one ormore systems of record instances of the data source provider from whichthe electronic activity was received. The data processing system 9300can further synchronize the electronic activity matched to recordobjects to update the system of record instances of the data sourceprovider (9350). Furthermore, the data processing system 9300 can usethe featurized data to provide performance predictions (9345) andgenerate other business process related outputs, insights andrecommendations.

As described herein, electronic activity can include any type ofelectronic communication that can be stored or logged. Examples ofelectronic activity can include electronic mail messages, telephonecalls, calendar invitations, social media messages, mobile applicationmessages, instant messages, cellular messages such as SMS, MMS, amongothers, as well as electronic records of any other activity, such asdigital content, such as files, photographs, screenshots, browserhistory, internet activity, shared documents, among others.

The electronic activity can be stored on one or more data sourceservers. The electronic activity can be owned or managed by one or moredata source providers, such as companies that utilize the services ofthe data processing system 9300. The electronic activity can beassociated with or otherwise maintained, stored or aggregated by anelectronic activity source, such as Google G Suite, Microsoft Office365,Microsoft Exchange, among others. In some embodiments, the electronicactivity can be real-time (or near real-time) electronic activity,asynchronous electronic activity (such as emails, text messages, amongothers) or synchronous electronic activity (such as meetings, phonecalls, video calls), or other activity in which two parties arecommunicating simultaneously.

1. Systems and Methods for Generating a Node Graph Using ElectronicActivities

As described above, the present disclosure relates to systems andmethods for constructing a node graph based on electronic activity. Thenode graph can include a plurality of nodes and a plurality of edgesbetween the nodes indicating activity or relationships that are derivedfrom a plurality of data sources that can include one or more types ofelectronic activities. The plurality of data sources can further includesystems of record, such as customer relationship management systems,enterprise resource planning systems, document management systems,applicant tracking systems or other source of data that may maintainelectronic activities, activities or records.

Referring now to FIG. 4, FIG. 4 illustrates a node graph generationsystem 200 for constructing a node graph based on electronic activity.The node graph generation system 200 can be, include or be part of thedata processing system 9300 described in FIG. 3. The node graphgeneration system 200 can include an electronic activity ingestor 205,an electronic activity parser 210, a source health scorer 215, a nodeprofile manager 220, a node profile database 225, a record dataextractor 230, an attribute value confidence scorer 235, a node pairingengine 240, a node resolution engine 245, an electronic activity linkingengine 250, a record object manager 255, a data source provider networkgenerator 260, a tagging engine 265 and a filtering engine 270. The nodegraph generation system 200 can receive electronic activity and systemsof record data from one or more data source providers 9350. The datasource providers can provide electronic activity or data stored ormaintained on a plurality of data sources 355 and one or more systems ofrecord 360.

Referring now to FIG. 5A, FIG. 5A illustrates an example electronicactivity or message. The electronic message 505 can identify one or morerecipients 510, one or more senders 512, a subject line 514, an emailbody 516, an email signature 518 and a message header 520. The messageheader can include additional information relating to the transmissionand receipt of the email message, including a time at which the emailwas sent, a message identifier identifying a message, an IP addressassociated with the message, a location associated with the message, atime zone associated with the sender, a time at which the message wastransmitted, received, and first accessed, among others. The electronicmessage 505 can include additional data in the electronic message 505 orin the header or metadata of the electronic message 505.

Referring now to FIG. 5B, FIG. 5B illustrates an example call entryrepresenting a phone call or other synchronous communication is shown.The call entry 525 can identify a caller 530, a location 532 of thecaller, a time zone 534 of the caller, a receiver 536, a location 538 ofthe receiver, a time zone 540 of the receiver, a start date and time542, an end date and time 544, a duration 546 and a list of participants548. In some embodiments, the times at which each participant joined andleft the call can be included. Furthermore, the locations from whicheach of the callers called can be determined based on determining if theuser called from a landline, cell phone, or voice over IP call, amongothers. The call entry 525 can also include fields for phone numberprefixes (e.g., 800, 866, and 877), phone number extensions, and callerID information.

Referring now to FIG. 5C, FIG. 5C illustrates an example calendar entry560. The calendar entry 560 can identify a sender 562, 564564564 a listof participants 564, a start date and time 566 location 532 of thecaller, an end date and time 568, a duration 570 of the calendar entry,a subject 572 of the calendar entry, a body 574 of the calendar entry,one or more attachments 576 included in the calendar entry and alocation of event 578, described by the calendar entry 560. The calendarentry can include additional data in the calendar entry or in the headeror metadata of the calendar entry 560.

In some embodiments, the electronic activities are exchanged between orotherwise involve nodes. In some embodiments, nodes can berepresentative of people or companies. In some embodiments, nodes can bemember nodes or group nodes. A member node may refer to a noderepresentative of a person that is part of a company or otherorganizational entity. A group node may refer to a node that isrepresentative of the company or other organizational entity and islinked to multiple member nodes. The electronic activity may beexchanged between member nodes in which case the system is configured toidentify the member nodes and the one or more group nodes associatedwith each of the member nodes. Each node can correspond to a nodeprofile. The node profile can include one or more field-value pairs thatrepresent the node.

The data processing system 9300 or the node graph generation system 200can be configured to assign each electronic activity a unique electronicactivity identifier. This unique electronic activity identifier can beused to uniquely identify the electronic activity. Further, eachelectronic activity can be associated with a source that provides theelectronic activity. In some embodiments, the data source can be thecompany or entity that authorizes the system 9300 or 200 to receive theelectronic activity. In some embodiments, the source can correspond to asystem of record, an electronic activity server that stores or manageselectronic activity, or any other server that stores or manageselectronic activity related to a company or entity. As will be describedherein, the quality, health or hygiene of the source of the electronicactivity may affect the role the electronic activity plays in generatingthe node graph. The node graph generation system 200 can be configuredto determine a time at which the electronic activity occurred. In someembodiments, the time may be based on when the electronic activity wastransmitted, received or recorded. As will be described herein, the timeassociated with the electronic activity can also affect the role theelectronic activity plays in generating the node graph.

Referring again to FIG. 4, additional details relating to the functionsperformed by various components or modules of the node graph generationsystem 200 are provided herein.

A. Electronic Activity Ingestion The electronic activity ingestor 205can be any script, file, program, application, set of instructions, orcomputer-executable code that is configured to enable a computing deviceon which the electronic activity ingestor 205 is executed to perform oneor more functions of the electronic activity ingestor 205 describedherein. The electronic activity ingestor 205 can be configured to ingestelectronic activities from the plurality of data source providers. Theelectronic activities may be received or ingested in real-time orasynchronously as electronic activities are generated, transmitted orstored by the one or more data source providers.

The node graph generation system 200 can ingest electronic activity froma plurality of different source providers. In some embodiments, the nodegraph generation system 200 can be configured to manage electronicactivities and one or more systems of record for one or moreenterprises, organizations, companies, businesses, institutions or anyother group associated with a plurality of electronic activity accounts.The node graph generation system 200 can ingest electronic activitiesfrom one or more servers that hosts, processes, stores or manageselectronic activities. In some embodiments, the one or more servers canbe electronic mail or messaging servers. The node graph generationsystem 200 can ingest all or a portion of the electronic activitiesstored or managed by the one or more servers. In some embodiments, thenode graph generation system 200 can ingest the electronic activitiesstored or managed by the one or more servers once or repeatedly on aperiodic basis, such as daily, weekly, monthly or any other frequency.

The node graph generation system 200 can further ingest other data thatmay be used to generate or update node profiles of one or more nodesmaintained by the node graph generation system 200. The other data mayalso be stored by the one or more servers that hosts, processes, storesor manages electronic activities. This data can include contact data,such as Names, addresses, phone numbers, Company information, titles,among others.

The node graph generation system 200 can further ingest data from one ormore systems of record. The systems of record can be hosted, processed,stored or managed by one or more servers of the systems of record. Thesystems of record can be linked or otherwise associated with the one ormore servers that host, process, store or manage electronic activities.In some embodiments, both the servers associated with the electronicactivities and the servers maintaining the systems of record may belongto the same organization or company.

The electronic activity ingestor 205 can receive an electronic activityand can assign each electronic activity, an electronic activity uniqueidentifier 502 to enable the node graph generation system 200 touniquely identify each electronic activity. In some embodiments, theelectronic activity unique identifier 502 can be the same identifier asa unique electronic activity identifier included in the electronicactivity. In some embodiments, the unique electronic activity isincluded in the electronic activity by the source of the electronicactivity or any other system.

The electronic activity ingestor 205 can be configured to format theelectronic activity in a manner that allows the electronic activity tobe parsed or processed. In some embodiments, the electronic activityingestor 205 can identify one or more fields of the electronic activityand apply one or more normalization techniques to normalize the valuesincluded in the one or more fields. In some embodiments, the electronicactivity ingestor 205 can format the values of the fields to allowcontent filters to apply one or more policies to identify one or moreregex patterns for filtering the content, as described herein.

It should be appreciated that the electronic activity ingestor 205 canbe configured to ingest electronic activities in a real-time or nearreal-time basis for accounts of one or more enterprises, organizations,companies, businesses, institutions or any other group associated with aplurality of electronic activity account with which the node graphgeneration system 200 has integrated. When an enterprise clientsubscribes to a service provided by the node graph generation system200, the enterprise client provides access to electronic activitiesmaintained by the enterprise client by going through an onboardingprocess. That onboarding process allows the system 200 to accesselectronic activities owned or maintained by the enterprise client fromone or more electronic activities sources. This can include theenterprise client's mail servers, one or more systems of record, one ormore phone services or servers of the enterprise client, among othersources of electronic activity. The electronic activities ingestedduring an onboarding process may include electronic activities that weregenerated in the past, perhaps many years ago, that were stored on theelectronic activities' sources. In addition, in some embodiments, thesystem 200 can be configured to ingest and re-ingest the same electronicactivities from one or more electronic activities sources on a periodicbasis, including daily, weekly, monthly, or any reasonable frequency.

The electronic activity ingestor 205 can be configured to receive accessto each of the electronic activities from each of these sources ofelectronic activity including the systems of record of the enterpriseclient. The electronic activity ingestor 205 can establish one or morelisteners, or other mechanisms to receive electronic activities as theyare received by the sources of the electronic activities enablingreal-time or near real-time integration.

As more and more data is ingested and processed as described herein, thenode graph generated by the node graph generation system 200 as well asnode profiles of nodes can get richer and richer with more information.The additional information, as will be described herein, can be used topopulate missing fields or add new values to existing fields, reinforcefield values that have low confidence scores and further increase theconfidence score of field values, adjust confidence scores of certaindata points, and identify patterns or make deductions based on thevalues of various fields of node profiles of nodes included in thegraph.

As more data is ingested, the node graph generation system 200 can useexisting node graph or node profile data to predict missing or ambiguousvalues in electronic activities such that the more node profiles anddata included in the node graph, the better the predictions of the nodegraph generation system 200, thereby improving the processing of theingested electronic activities and thereby improving the quality of eachnode profile of the node graph, which eventually will improve thequality of the overall node graph of the node graph generation system200.

The node graph generation system 200 can be configured to periodicallyregenerate or recalculate the node graph. The node graph generationsystem 200 can do so responsive to additional data being ingested by thesystem 200. When new electronic activities or data is ingested by thenode graph generation system 200, the system 200 can be configured torecalculate the node graph as the confidence scores (as will bedescribed later) can change based on the information included in the newelectronic activities. In some embodiments, the ingestor may re-ingestpreviously ingested data from the one or more electronic activitysources or simply ingest the new electronic activity not previouslyingested by the system 200.

B. Electronic Activity Parsing

The electronic activity parser 210 can be any script, file, program,application, set of instructions, or computer-executable code, which isconfigured to enable a computing device on which the electronic activityparser 210 is executed to perform one or more functions of theelectronic activity parser 210 described herein.

The electronic activity parser 210 can be configured to parse theelectronic activity to identify one or more values of fields to be usedin generating node profiles of one or more nodes and associate theelectronic activities between nodes for use in determining theconnection and connection strength between nodes. The node profiles caninclude fields having name-value pairs or field-value pairs. Theelectronic activity parser 210 can be configured to parse the electronicactivity to identify values for as many fields of the node profiles ofthe nodes with which the electronic activity is associated.

The electronic activity parser 210 can be configured to first identifyeach of the nodes associated with the electronic activity. In someembodiments, the electronic activity parser 210 can parse the metadataof the electronic activity to identify the nodes. The metadata of theelectronic activity can include a To field, a From field, a Subjectfield, a Body field, a signature within the body and any otherinformation included in the electronic activity header that can be usedto identify one or more values of one or more fields of any node profileof nodes associated with the electronic activity. In some embodiments,non-email electronic activity can include meetings or phone calls. Themetadata of such non-email electronic activity can include a duration ofthe meeting or call, one or more participants of the meeting or call, alocation of the meeting, locations associated with the initiator andreceiver of the phone call, in addition to other information that may beextracted from the metadata of such electronic activity. In someembodiments, nodes are associated with the electronic activity if thenode is a sender of the electronic activity, a recipient of theelectronic activity, a participant of the electronic node, or identifiedin the contents of the electronic activity. The node can be identifiedin the contents of the electronic activity or can be inferred based oninformation maintained by the node graph generation system 200 and basedon the connections of the node and one or more of the sender orrecipients of the electronic activity.

The electronic activity parser 210 can be configured to parse theelectronic activity to identify attributes, values, or characteristicsof the electronic activity. In some embodiments, the electronic activityparser 210 can apply natural language processing techniques to theelectronic activity to identify regex patterns, words or phrases, orother types of content that may be used for sentiment analysis,filtering, tagging, classifying, deduplication, effort estimation, andother functions performed by the data processing system 9300.

In some embodiments, the electronic activity parser 210 can beconfigured to parse an electronic activity to identify values of fieldsor attributes of one or more nodes. For instance, when an electronicmail message is ingested into the node graph generation system 200, theelectronic activity parser 210 can identify a FROM field of theelectronic mail message. The FROM field can include a name and an emailaddress. The name can be in the form of a first name and a last name ora last name, first name. The parser can extract the name in the FROMfield and the email address in the FROM field to determine whether anode is associated with the sender of the electronic mail message.

C. Signature Parsing

In some embodiments, the electronic activity parser 210 can beconfigured to identify a signature in a body of an electronic message.The parser 210 can identify the signature by utilizing a signaturedetection policy that includes logic for identifying patterns ofsignatures. In some embodiments, a signature can include one or morevalues of attributes, such as values for attributes including but notlimited to a name, a phone number, a company name, a company division, acompany address, a job title, one or more social network handles orlinks, an email address, among others. By parsing the signature, theelectronic activity parser 210 can identify each of the valuescorresponding to the various fields of a node profile associated withthe sender of the electronic activity. In addition to informationincluded in the signature, the electronic activity parser can utilizeinformation from the header of the electronic activity (i.e. first andlast name) to identify where the signature is located by finding thesame first name, last name and email address within a predeterminedproximity or distance of each other in a region of the body, forinstance, the bottom of the body. Stated in another way, the presentdisclosure describes methods and systems for utilizing header data of anelectronic activity, which in certain embodiments, is verified to makeit easier to locate a signature of an email, which may be buried under,around or with other textual content. In some embodiments, one or moreof a first name, a last name and an email address extracted from theheader of the electronic activity is used to identify text strings thatmatch the extracted first name, last name and the email address.Responsive to determining that text strings matching the first name,last name and the email address are within a predetermined distance ofone another, the parser 210 can identify the text strings are portionsof the signature of the electronic activity. The information parsed fromthe signature can be used to determine a confidence score of a value ofa field as further described herein with respect to the attribute valueconfidence scorer 235. The electronic activity parser 210 can also usesignature parsing for node selection and in the identification of thenode, to which the activity, containing the signature can be associated.

D. Node Profiles

The node profile manager 220 can be any script, file, program,application, set of instructions, or computer-executable code that isconfigured to enable a computing device on which the node profilemanager 220 is executed to perform one or more functions of the nodeprofile manager 220 described herein. The node profile manager isconfigured to manage node profiles associated with each node. Nodeprofiles of nodes can be used to construct a node graph that includesnodes linked to one another based on relationships between the nodesthat can be determined from electronic activities parsed and processedby the node graph generation system 200 as well as other informationthat may be received from one or more systems of record.

Referring now to FIG. 6A, FIG. 6A illustrates a representation of a nodeprofile of a node. The node profile 600 can include a unique nodeprofile identifier 602 and one or more fields or attributes 610 a-610 n.Each field 610 can include one or more value data structures 615. Eachvalue data structure can include a value 620, an occurrence metric 625,a confidence score 630 and one or more entries 635 a-n. Each entry 635can identify a data source 640 from which the value was identified (forinstance, a source of a system of record or a source of an electronicactivity), a number of occurrences of the value that appear in theelectronic activity, a time 645 associated with the electronic activity(for instance, at which time the electronic activity occurred) and anelectronic activity unique identifier 502 identifying the electronicactivity. In some embodiments, the occurrence metric 625 can identify anumber of times that value is confirmed or identified from electronicactivities or systems of record. The node profile manager 220 can beconfigured to update the occurrence metric each time the value isconfirmed. In some embodiments, the electronic activity can increase theoccurrence metric of a value more than once. For instance, for a fieldsuch as name, the electronic activity parser can parse multiple portionsof an electronic activity. In some embodiments, parsing multipleportions of the electronic activity can provide multiple confirmationsof, for example, the name associated with the electronic activity.

The node profile manager 220 can be configured to maintain a nodeprofile for each node that includes a time series of data points forevery value data structure 615 that are generated based on electronicactivities identifying the respective node. The node profile manager 220can maintain, for each field of the node profile, one or more valuesdata structures 615. The node profile manager 220 can maintain aconfidence score for each value of the field. As will be describedherein the confidence score of the value can be determined usinginformation relating to the electronic activities or systems of recordthat contribute to the value. The confidence score for each value canalso be based on the below-described health score of the data sourcefrom which the value was received. Further, the node profile manager 220can maintain an occurrence metric that identifies a number of timeselectronic activities or systems of record have contributed to thevalue. In some embodiments, the occurrence metric is equal to or greaterthan the number of electronic activities or systems of record thatcontribute to the value. In some embodiments, the system 200 or the nodeprofile manager 220 can determine that the electronic activity cancontribute to the value by generating an activity field-value pair thathas a value that matches the value of the value data structurecorresponding to the field of the node profile. In some embodiments, thesystem 200 or the node profile manager 220 can determine that theelectronic activity can contribute to the value by parsing theelectronic activity to determine an inference that corresponds to thevalue. The node profile manager 220 further maintains an array includingthe plurality of entries 635 for each value. As more and more electronicactivities and data from more systems of record are ingested by the nodegraph generation system 200, values of each of the fields of nodeprofiles of nodes will become more enriched thereby further refining theconfidence score of each value.

In some embodiments, the node profile can include different types offields for different types of nodes. Member nodes and group nodes mayhave some common fields but may also include different fields. Further,member nodes may include fields that get updated more frequently thangroup nodes. Examples of some fields of member nodes can include i)First name; ii) Last name; iii) Email; iv) job title; v) Phone; vi)Social media handle; vii) LinkedIn URL; viii) website; among others.Each of the fields can be a multidimensional array, such as a3-dimensional array. In some embodiments, each field corresponds to oneor more name value pairs, where each field is a name and each value forthat field is a value. Examples of some fields of group nodes caninclude i) Company or Organization name; ii) Address of Company; iii)Phone; iv) Website; v) Social media handle; vi) LinkedIn handle; amongothers. Each of the fields can be a multidimensional array, such as a3-dimensional array. In some embodiments, each field corresponds to oneor more name value pairs, where each field is a name and each value forthat field is a value.

The node profile manager 220 can maintain, for each field of each nodeprofile, a field data structure that can be stored as a multidimensionalarray. The multidimensional array can include a dimension relating todata points that identify a number of electronic activities or system ofrecords that contribute to the field or the value of the field. Anotherdimension can identify the source, which can have an associated trustscore that can be used to determine how much weight to assign to thedata point from that source. Another dimension can identify a time atwhich the data point was generated (for instance, in the case of a datapoint derived from an electronic activity such as an email, the time thedata point was generated can be the time the electronic activity wassent or received). In the case of a data point being derived from asystem of record, the time the data point was generated can be the timethe data point can be entered into the system of record or the time thedata point was last accessed, modified, confirmed, or otherwisevalidated in or by the system of record. These dimensions are all usedto determine a confidence score of the value as will be describedherein. In some embodiments, the node profile manager 220 can assign acontribution score to each data point. The contribution score can beindicative of the data point's contribution towards the confidence scoreof the value. The contribution score of a data point can decay over timeas the data point becomes staler. The contribution scores of each of thedata points derived from electronic activities and systems of record canbe used to compute the confidence score of the value of a field of thenode profile.

Referring now to FIG. 6B, FIG. 6B illustrates a representation of threeelectronic activities and a representation of three states of a nodeprofile of a node according to embodiments of the present disclosure. Asshown in FIG. 6B, three electronic activities sent at a first time, asecond time and third time are shown. The first electronic activity 652a includes or is associated with a first electronic activity identifier654 a (“EA-003”). The second electronic activity 652 b includes or isassociated with a second electronic activity identifier 654 b(“EA-017”). The third electronic activity 652 c includes or isassociated with a third electronic activity identifier 654 b (“EA-098”).Collectively, the electronic activities can be referred to herein aselectronic activities 652 or individually as electronic activity 652.Each electronic activity can include corresponding metadata, asdescribed above, a body, and a respective signature 660 a-c included inthe body of the respective electronic activity 652. As shown in FIG. 6B,each of the signatures 660 a-c is different from the others.

FIG. 6B also includes three different representations of a node profilecorresponding to three different times. The node profile corresponds toa node profile of the sender of the electronic activities 652 asdetermined by the node profile manager 220. The first representation 662a of the node profile was updated after the first electronic activity652 a was ingested by the node graph generation system 200 but beforethe second and third electronic activities 652 b and 652 c were ingestedby the system 200. The second representation 662 b of the node profilewas updated after the first and second electronic activities 652 a and652 b were ingested by the node graph generation system 200 but beforethe third electronic activity 652 c was ingested by the system 200. Thethird representation 662 c of the node profile was updated after allthree electronic activities 652 were ingested by the node graphgeneration system 200.

Each of the representations 662 of the node profile can include fieldsand corresponding values. For example, in the first representation 662a, the field “First Name” is associated with 2 different values, Johnand Johnathan. The first representation 662 a also includes the field“Title” which is associated with the value “Director.” In contrast, thesecond representation 662 b and the third representation 662 c bothinclude an additional value “CEO” for the field “Title.” Furthermore, inthe third representation 662 c, the field “Company Name” is associatedwith 2 different values, Acme and NewCo in contrast with the first tworepresentations 662 a and 662 b of the node profile. The values of thefield Last Name and Cell Phone Number remain the same in all threerepresentations 662 of the node profile.

Each of the values included in the node profile can be supported by oneor more data points. Data points can be pieces of information orevidence that can be used to support the existence of values of fieldsof node profiles. A data point can be an electronic activity, a recordobject of a system of record (as will be described herein), or otherinformation that is accessible and processable by the system 200. Insome embodiments, a data point can identify an electronic activity, arecord object of a system of record (as will be described herein), orother information that is accessible and processable by the system 200that serves as a basis for supporting a value in a node profile. Eachdata point can be assigned its own unique identifier. Each data pointcan be associated with a source of the data point identifying an originof the data point. The source of the data point can be a mail server, asystem of record, among others. Each of these data points can alsoinclude a timestamp. The timestamp of a data point can identify when thedata point was either generated (in the case of an electronic activitysuch as an email) or the record object that serves as a source of thedata point was last updated (in the case when the data point isextracted from a system of record). Each data point can further beassociated with a trust score of the source of the data point. The trustscore of the source can be used to indicate how trustworthy or reliablethe data point is. The data point can also be associated with acontribution score that can indicate how much the data point contributestowards a confidence score of the value associated with the data point.The contribution score can be based on the trust score of the source(which is based in part on a health score of the source) and a time atwhich the data point was generated or last updated.

A confidence score of the value can indicate a level of certainty thatthe value of the field is a current value of the field. The higher theconfidence score, the more certain the value of the field is the currentvalue. The confidence score can be based on the contribution scores ofindividual data points associated with the value. The confidence scoreof the value can also depend on the corresponding confidence scores ofother values of the field, or the contribution scores of data pointsassociated with other values of the field.

The table below illustrates various values for various fields andincludes an array of data points that contribute to the respectivevalue. As shown in the table, the same electronic activity can serve asdifferent data points for different values. Further, the tableillustrates a simplified form for the same of convenience andunderstanding.

Different values can be supported by different number of data points.The three electronic activities shown in FIG. 6B (652 a-c) are includedin the table below. Using the table and the representations 662 a-c ofthe node profile, one can understand how the system 200 is capable ofdetermining values of fields of node profiles and changes to nodeprofiles as more electronic activities and data points are processed bythe system 200.

The signature 660 b is different from the signature 660 a in that thetitle of the person John Smith has changed from Director to CEO. Thedata points supporting or contributing the value Director include thefirst electronic activity 652 a but not the second electronic activity652 b. Also, the data points include information received from systemsof records including data points that correspond to time periods afterthe value is no longer accurate. For instance, the data point DP ID225is a data point supporting the value “Director” for the node profileeven though person has been promoted to CEO. The system 200 isconfigured to process and accept all data points but can assigndifferent contribution scores based on the source of the data point andallow the system 200 to accurately maintain a state of the node profileeven if some of the data that is received may be inaccurate or stale.

As will be described further below, it can be challenging to matchelectronic activities to node profiles. The system 200 can match thethird electronic activity 652 c to the node profile corresponding to thenode profile representation 662 even though the electronic activityidentified a different email address, a different company name, and adifferent office number. In some embodiments, the system 200 candetermine, by parsing the electronic activity, information about thesender that can be used to identify the correct node profile. In thisparticular case, the system 200 can rely on the first name, last name,and cell phone number (which is generally unique) to map the electronicactivity to the correct node profile 662 as opposed to other nodeprofiles including the name John Smith. Table 1:

Trust Contribution Data Point # DP ID TimeStamp Activity ID Source ScoreScore Field: First Name Value: John [Confidence score] = 0.8 Data Point1: DP ID101 2/1/2016 4 pm ET EA-003 Email 100 0.6 Data Point 2: DP ID2252/18/2017 2pm ET SOR-012 CRM 70 0.4 Data Point 3: DP ID343 3/1/2018 1 pmET EA-017 Email 100 0.7 Data Point 4: DP ID458 7/1/2018 3 pm ET EA-098Email 100 0.8 Data Point 5: DP ID576 9/12/2015 3 pm ET SOR-145 Talend 200.2 Field: First Name Value: Johnathan [Confidence score] = 0.78 DataPoint 1: DP ID101 2/1/2016 4 pm ET EA-003 Email 100 0.6 Data Point 2: DPID225 2/18/2017 2pm ET SOR-012 CRM 70 0.4 Data Point 3: DP ID3433/1/2018 1 pm ET EA-017 Email 100 0.7 Data Point 4: DP ID458 7/1/2018 3pm ET EA-098 Email 100 0.8 Data Point 5: DP ID576 9/12/2015 3 pm ETSOR-145 Talend 20 0.2 Field: Title Value: Director [Confidence score] =0.5 Data Point 1: DP ID101 2/1/2016 4 pm ET EA-003 Email 100 0.6 DataPoint 2: DP ID225 2/18/2017 2pm ET SOR-012 CRM 70 0.4 Data Point 3: DPID243 3/1/2017 1 pm ET EA-117 Email 100 0.65 Data Point 4: DP ID2433/1/2018 1 pm ET SOR-087 CRM 5 0.05 Field: Title Value: CEO [Confidencescore] = 0.9 Data Point 1: DP ID343 3/1/2018 1 pm ET EA-017 Email 1000.7 Data Point 2: DP ID458 7/1/2018 3 pm ET EA-098 Email 100 0.8 DataPoint 3: DP ID225 3/18/2018 2pm ET SOR-015 CRM 65 0.54 Field: CompanyValue: Acme [Confidence score] = 0.6 Data Point 1: DP ID101 2/1/2016 4pm ET EA-003 Email 100 0.6 Data Point 2: DP ID225 2/18/2017 2pm ETSOR-012 CRM 70 0.4 Data Point 3: DP ID343 3/1/2018 1 pm ET EA-017 Email100 0.7 Field: Company Value: NewCo [Confidence score] = 0.9 Data Point1: DP ID458 7/1/2018 3 pm ET EA-098 Email 100 0.8 Data Point 2: DP ID6547/18/2018 2pm ET EA-127 Email 100 0.85 Data Point 3: DP ID876 8/1/2018 1pm ET EA-158 Email 100 0.9 Field: Cell Phone Value: 617-555-2000[Confidence score] = 0.95 Data Point 1: DP ID101 2/1/2016 4 pm ET EA-003Email 100 0.6 Data Point 2: DP ID225 2/18/2017 2pm ET SOR-012 CRM 70 0.4Data Point 3: DP ID343 3/1/2018 1 pm ET EA-017 Email 100 0.7 Data Point4: DP ID458 7/1/2018 3 pm ET EA-098 Email 100 0.8 Data Point 5: DP ID5769/12/2015 3 pm ET SOR-145 Talend 20 0.2 Data Point 6: DP ID654 7/18/20182pm ET EA-127 Email 100 0.85 Data Point 7: DP ID876 8/1/2018 1 pm ETEA-158 Email 100 0.9

As a result of populating values of fields of node profiles usingelectronic activities, the node profile manager 220 can generate a nodeprofile that is unobtrusively generated from electronic activities thattraverse networks. In some embodiments, the node profile manager 220 cangenerate a node profile that is unobtrusively generated from electronicactivities and systems of record.

As described herein, the present disclosure relates to methods andsystems for assigning contribution scores to each data point (forexample, electronic activity) that contributes to a value of a fieldsuch that the same electronic activity can assign different contributionscores to different values of a single node profile and of multiple nodeprofiles. The contribution score can be based on a number of differentelectronic activities contributing to a given value of a field of a nodeprofile, a recency of the electronic activity, among others. In someembodiments, a system of record of an enterprise accessible to the nodegraph generation system can include data that can also contribute to avalue of a field of a node profile. The contribution score can be basedon a trust score or health score of the system of record, a number ofdifferent electronic activities or systems of record contributing to thevalue of the field of the node profile, a number of different electronicactivities or systems of record contributing to other values of thefield of the node profile, a recency of the value being confirmed by thesystem of record, among others.

In some embodiments, a method of updating confidence scores of values offields based on electronic activity includes associating the electronicactivity to a first value of a first field, assigning a firstcontribution score to the first value, associating the electronicactivity to a second value of a second field, assigning a secondcontribution score to the second value, and updating a confidence scoreof the first value and the second value based on the first contributionscore and the second contribution score.

Furthermore, the present disclosure relates to methods and systems formaintaining trust scores for sources and adjusting a contribution scoreof a data point for one or more values of fields of node profiles basedon the trust score of a source.

E. Matching Electronic Activity to Node Profiles

The node profile manager 220 can be configured to manage node profilesby matching electronic activities to one or more node profiles.Responsive to the electronic activity parser 210 parsing the electronicactivity to identify values corresponding to one or more fields orattributes of node profiles, the node profile manager 220 can apply anelectronic activity matching policy to match electronic activities tonode profiles. In some embodiments, the node profile manager 220 canidentify each of the identified values corresponding to a sender of theelectronic activity to match the electronic activity to a node profilecorresponding to the sender.

Using an email message as an example of an electronic activity, the nodeprofile manager 220 may first determine if the parsed values of one ormore fields corresponding to the sender of the email message matchcorresponding values of fields. In some embodiments, the node profilemanager 220 may assign different weights to different fields based on auniqueness of values of the field. For instance, email addresses may beassigned greater weights than first names or last names or phone numbersif the phone number corresponds to a company.

In some embodiments, the node profile manager 220 can use data from theelectronic activity and one or more values of fields of candidate nodeprofiles to determine whether or not to match the electronic activity toone or more of the candidate node profiles. The node profile manager 220can attempt to match electronic activities to one or more node profilesmaintained by the node profile manager 220 based on the one or morevalues of the node profiles. The node profile manager 220 can identifydata, such as strings or values from a given electronic activity andmatch the strings or values to corresponding values of the nodeprofiles. In some embodiments, the node profile manager 220 can computea match score between the electronic activity and a candidate nodeprofile by comparing the strings or values of the electronic activitymatch corresponding values of the candidate node profile. The matchscore can be based on a number of fields of the node profile including avalue that matches a value or string in the electronic activity. Thematch score can also be based on different weights applied to differentfields. The weights may be based on the uniqueness of values of thefield, as mentioned above. The node profile manager 220 can beconfigured to match the electronic activity to the node with thegreatest match score. In some embodiments, the node profile manager canmatch the electronic activity to each candidate node that has a matchscore that exceeds a predetermined threshold. Further, the node profilemanager 220 can maintain a match score for each electronic activity tothat particular node profile, or to each value of the node profile towhich the electronic activity matched. By doing so, the node profilemanager 220 can use the match score to determine how much weight toassign to that particular electronic activity. Stated in another way,the better the match between the electronic activity and a node profile,the greater the influence the electronic activity can have on the values(for instance, the contribution scores of the data point on the valueand as a result, in the confidence scores of the values) of the nodeprofile. In some embodiments, the node profile manager 220 can assign afirst weight to electronic activities that have a first match score andassign a second weight to electronic activities that have a second matchscore. The first weight may be greater than the second weight if thefirst match score is greater than the second match score. In someembodiments, if no nodes are found to match the electronic activity orthe match score between the email message and any of the candidate nodeprofiles is below a threshold, the node profile manager 220 can beconfigured to generate a new node profile to which the node profilemanager assigns a unique node identifier 602. The node profile manager220 can then populate various fields of the new node profile from theinformation extracted from the electronic activity parser 210 after theparser 210 parses the electronic activity.

In addition to matching the electronic activity to a sender node, thenode profile manager is configured to identify each of the nodes towhich the electronic activity can be matched. For instance, theelectronic activity can be matched to one or more recipient nodes usinga similar technique except that the node profile manager 220 isconfigured to look at values extracted from the TO field or any otherfield that can include information regarding the recipient of the node.In some embodiments, the electronic activity parser can be configured toparse a name in the salutation portion of the body of the email toidentify a value of a name corresponding to a recipient node. In someembodiments, the node profile manager 220 can also match the electronicactivity to both member nodes as well as the group nodes to which themember nodes are identified as members.

In some embodiments, the electronic activity parser 210 can parse thebody of the electronic activity to identify additional information thatcan be used to populate values of one or more node profiles. The bodycan include one or more phone numbers, addresses, or other informationthat may be used to update values of fields, such as a phone numberfield or an address field. Further, if the contents of the electronicactivity include a name of a person different from the sender orrecipient, the electronic activity parser 210 can further identify oneor more node profiles matching the name to predict a relationshipbetween the sender and/or recipient of the electronic activity and anode profile matching the name included in the body of the electronicactivity.

The node profile manager 220 can be configured to maintain a nodeprofile data structure that maintains separate values for the samefield. For instance, the electronic message can be destined tojohn.smith@example.com <Johnathan Smith> and the body of the emailstates “Dear Johnathan”. The parser can be configured to identify afirst name, a last name and an email address for the recipient applyinglogic to specific portions of the electronic activity. In certainembodiments, the node profile manager 220 can be configured to runstatistical analysis of all nodes and determine that John is a verycommon name and thus identify that this node not only has Johnathan asfirst name but also John is the other First Name value. Moreover, thenode profile manager 220 can be configured to determine if a value of afield is unique enough to match the electronic activity to the nodebased on the value of the field. If the value of the field does not meeta predetermined threshold, other values of fields may be used to matchthe electronic activity to a given node. In addition, values of fieldsmay be prioritized for matching the electronic activity to the node. Forinstance, the name John is relatively common and as such, attempting tomatch an electronic activity to a node using the value “John” for thefield “First Name” may be less dispositive than matching a more uniquevalue, such as an email address. In some embodiments, the node profilemanager 220 can weigh fields that have values that are relatively moreunique higher than fields that have values that are relatively lessunique when matching an electronic activity to a node. In someembodiments, the node profile manager 220 can be configured to restrictmatching electronic activities to nodes using values of fields that aredetermined to not be sufficiently unique.

The node profile manager 220 can be configured to identify a node thathas fields having values that match the values included in the nodeprofile of the node. To do so, the node profile manager may determinethatjohn.smith@example.com belongs to only one node. The node profilemanager can then select that node to be the recipient of the emailmessage. The node profile manager would then populate each of the fieldsof the node profile with an entry for each value of each respectivefield that was identified by the electronic activity parser 210. Inparticular, the node profile manager can generate, for each value of afield that is identified by the electronic activity parser 210, an entryin that value data structure that identifies the electronic activity, asource of the electronic activity, a time associated with the electronicactivity and a number of occurrences within the electronic activity thatinclude the value. In the email message described above, the nodeprofile manager can update the value data structure of the Name field ofthe recipient node with an entry that identifies the source of theemail, the time associated with the email and a total number ofoccurrences of the value in the email. In this case, the total number ofoccurrences was 2 because the first name of the recipient was listed asJohnathan and the salutation identified the name Johnathan.

Referring briefly to FIG. 7, FIG. 7 illustrates a series of electronicactivities between two nodes, N1 702 and N2 704. N1 702 may correspondto a node associated with an entity whose electronic activities areingested by the node graph generation system 200, while node N2 704 maycorrespond to a node external to the entity associated with the node N1.A node profile 715 for node N2 is maintained by the node profile manager220. Before the electronic activity 710 was ingested by the node graphgeneration system 200, the node profile included the five fields, name,email, phone, company and job title. This information was previouslyincluded in the node profile and may have been determined by ingestinginformation from a system of record. At that time, the confidence scoreof each of the fields is 1. When the first electronic activity isingested by the system 200, the node profile manager can update the nodeprofile 715 and increase the confidence score of values of fields thatcan be verified by the electronic activity. By virtue of the electronicactivity being successfully transmitted from N1 to N2, the node profilemanager 220 can update the confidence score of the email valuej@acme.com and the company name Acme by parsing the email address anddetermining that the domain name of the email matches a domain name ofthe company node, to which N2 belongs. In some embodiments, the nodeprofile manager 220 may determine that the electronic activity issuccessfully transmitted by determining that the N1 did not receive abounce back electronic activity that indicates that the electronicactivity was not successfully transmitted. Examples of bounce backelectronic activity can include emails indicating that the destinationemail address is invalid or incorrect, the person is no longer withcompany, among others.

In some embodiments, the node graph generation system 200 can, via theelectronic activity parser or through some other module, parse bounceback electronic activities to determine a reason for why the electronicactivity bounced back. In some embodiments, the node graph generationsystem 200 can use natural language processing to determine a cause forthe bounce back activity. In this way, the node graph generation system200 can determine if an email address associated with a person or nodeis still valid or if it is incorrect or if the person is no longerassociated with the company identified by the domain of the emailaddress.

Node N2 can then send back a response email to node N1 that includes asignature 726 in the body of the electronic activity. The node profilemanager can update, from the successful transmission of the emailresponse and the parsing of the signature, the node profile of N2 byincreasing the confidence score of the name of John Smith, the titlefrom the signature, the company name 2 times (one of which was derivedby matching the domain name of the email to the domain name of the groupnode in the node graph) as it is included in the email address and inthe signature, and further add a new value for the phone number, whichis extracted from the signature. The extracted phone number canrepresent his direct office number, while the phone number previouslymaintained in the node profile can be a general company number. In someembodiments, the system can be configured to classify phone numbers as ageneral company number or a direct office number based on the frequencyof the number appearing in different node profiles. In some embodiments,the node graph generation system 200 can be configured to classify phonenumbers as a general company number or a direct office number byperforming regex patterns to determine if an “ext.” or an “x” followedby some numbers is included in the value. The regex can also beconfigured to identify phone number prefixes, such as “800.” The systemcan identify the phone numbers as the publicly known phone number of thecompany. In some embodiments, the node graph generation system 200 canbe configured to restrict or otherwise prevent a phone number determinedto be a general company number from being inserted as a value of apersonal number. In some embodiments, the node graph generation system200 can be configured to determine the value of phone numbers of othernodes corresponding to the same company and if the system determinesthat the number to be added to a node matches the number of multipleother nodes belonging to the same entity or company, the system canprobabilistically determine, for instance, that the number is a worknumber and update the number as a value in the work number field(instead of a personal number field). Similar techniques can be appliedfor determining or inferring other information by comparing the data ofa node profile to patterns observed from a plurality of related nodeprofiles. In some embodiments, the system can determine whether thefirst predetermined digits (for instance, the first 6 digits) areidentical to the first predetermined digits of phone numbers of othernodes belonging to the same company. If the first predetermined digitsof the number match the first predetermined digits of phone numbers ofother nodes belonging to the same company, the system can determine thatthe number is a work number. Similarly, an address extracted from asignature can be determined to be a work address if the address matchesthe address of other nodes belonging to the same entity or company. Inthis way, any value of a field of a node extracted can be determined tobe specific to a company if other nodes corresponding to peoplebelonging to the company also include the same value for the field orinter-related values in other fields. Additional details regardingincreasing or adjusting the confidence score of various values of fieldsof node profiles based on occurrences of electronic activities areprovided herein.

Generally, the node profile manager 220 can attempt to match electronicactivities, such as emails, to node profiles based on an email address.However, in some instances, a user may send or receive an email addressfrom a second email address, such as a personal email address instead ofa work email address. The node profile manager 220 can analyze theelectronic activity and look at other signals from the electronicactivity to see if the electronic activity should be matched to apreviously established node profile that corresponds to the user thatdoes not include the second email address instead of a creating a newnode profile based on the second email address.

For instance, the node profile manager 220 can be configured to identifyan email that includes an email address john.smith@gmail.com. The nodeprofile manager 220 can determine that either no node profile includesthe john.smith@gmail.com as a value of an email address field or even ifthe email appears as a value in the email address field of a nodeprofile, the confidence score of the value of the email address is belowa certain threshold sufficient for the node profile manager 220. In someembodiments, the node profile manager 220 can apply one or more policiesor rules for generating new nodes. For instance, the node profilemanager 220 can implement an email address based node profile generationpolicy in which the system is configured to not create new node profilesif the email address corresponds to an email address of one or morepredefined email systems. For instance, the email address based nodeprofile generation policy can include one or more rules for generatingnew node profiles or restricting the generation of new node profiles. Insome embodiments, the node profile generation policy can restrict thecreation or generation of new node profiles if the email addresscorresponds to an email address of one or more predefined email systems.For instance, the predefined email systems can include email systemsthat provide “free” email addresses like “gmail.com” or “yahoo.com”. Insuch cases, the node profile manager 220 can be configured to use othersignals from the electronic activity to attempt to match the electronicactivity to a node profile for which the email address did not provide amatch to a node profile. The node profile manager 220 can use fuzzymatching techniques including a first name, last name, email addressprefix, a phone number or any other information that can be extractedfrom the email address to match the electronic activity to an existingnode profile. In some embodiments, the node profile manager 220 can alsoidentify other node profiles to which the electronic activity can bematched and identify likely node profiles based on connection strengthsbetween the node profiles to which the electronic activity can bematched and the one or more likely node profiles.

As discussed above, in the case that John Smith inadvertently sent anemail from his Gmail address as opposed to his company email address,john.smith@example.com, the node profile manager 220 can use one or moreof the first name, last name, phone number or other information includedin the signature of the email to match the electronic activity to a nodeprofile that includes the email address, john.smith@example.com. In thisway, if other signals are pointing or expecting a work email address,the electronic activity will be matched to the node profile with thework email address. The system can determine additional signals from theelectronic activity. For instance, the system can parse the electronicactivity to determine if the electronic activity includes text orstrings that match one or more predetermined strings or keywords thatare mapped to the person's work. For instance, the predeterminedkeywords can include product names of his company, his company's name,among others. In addition, the system can identify one or moreparticipants of the electronic activity and determine if any of theparticipants correspond to node profiles with which the person (John)has had exchanged electronic activities in the past.

F. Node Profile Value Prediction and Augmentation

The node profile manager 220 can be configured to augment node profileswith additional information that can be extracted from electronicactivities or systems of record or that can be inferred based on othersimilar electronic activities or systems of record. In some embodiments,the node profile manager 220 can determine a pattern for various fieldsacross a group of member nodes (such as employees of the same company).For instance, the node profile manager 220 can determine, based onmultiple node profiles of member nodes belonging to a group node, thatemployees of a given company are assigned email addresses following agiven regex pattern. For instance, [first name].[last name]@[companydomain].com. As such, the node profile manager 220 can be configured topredict or augment a value of a field of a node profile of an employeeof a given company when only certain information of the employee isknown by the node profile manager 220.

First Last Company Name Name Name Email address John Smith Examplejohn.smith@example.com George Baker Example george.baker@example.comAdam Jones Example (unknown) adam.jones@exampl.com (predicted) (unknown)(unknown) Example linda.chan@example.com Linda Chan (predicted)(predicted)

As shown in the table above, the node profile manager 220 can beconfigured to determine that the email address for Adam Jones isadam.jones@example.com by observing a regex pattern the company Exampleuses when assigning email addresses to its employees. In someembodiments, the node profile manager 220 can update the email addressfield of Adam Jones accordingly. In some embodiments, the node profilemanager 220 can be configured to transmit an email toadam.jones@example.com to check whether the email address is valid or ifa bounce back email is received. If no bounce back email is receivedindicating that the email address is not valid or cannot be found, theconfidence score of adam.jones@example.com can increase even though theemail address was unknown to the node graph generation system 200 basedon the electronic activities ingested by the system 200.

Similarly, the node profile manager 220 can infer the first and lastnames of people having email addresses corresponding to a company byparsing information using the known regex patterns. As shown above, thenode profile manager 220 can predict that the name of the personassociated with the email address linda.chan@example.com is Linda Chanbased on the regex pattern observed from other known node profilesmaintained by the node profile manager 220. In some embodiments, thenode profile manager 220 can infer the first and last names of peoplehaving email addresses corresponding to a company by also using otherdata points in the electronic activity, such as parsing email headermetadata, email signature, or a greeting at the top of the email body tocorrelate with and confirm the name, predicted from the regex patternabove. As previously described with respect to the descriptionassociated with Table 1, the system can rely on multiple data points tomatch an electronic activity to a particular node profile (for instance,relying in part on the cell phone number included in the signature asdiscussed with respect to Table 1). In this way, further confirmation ofthe inference of the first name and/or last name can be obtained,thereby improving the accuracy of the node profile and the overall nodegraph. It should further be appreciated that if multiple people have thesame name or initials, the company may assign alternate email addressnaming conventions for such people. For instance, a company may includea middle initial in the email address for person if the email addressgenerated using the company's primary regex pattern for assigning emailaddresses is already taken. In such cases, the node profile manager 220may again further rely on other data points in the electronic activity,such as parsing email header metadata, email signature, or a greeting atthe top of the email body to infer the first and last names of theperson.

In this way, by knowing the regex patterns of email addresses assignedby a company, the node profile manager 220 can be configured to predictemail addresses of people at the company for which we have someinformation. Furthermore, if an email address is known, we can predictother information not otherwise known based on the email address. Insome embodiments, even if some information is known, the confidencescore of that information can be updated based on the node profilemanager 220 being configured to predict certain values.

In some embodiments, the node profile manager 220 can be configured tomaintain both work and personal phone numbers and work and personalgeographical locations of node profiles. The node profile manager 220can be configured to determine if a phone number extracted from anelectronic activity is a work phone number or a personal phone numberthrough one or more verification techniques. In some embodiments, thenode profile manager 220 can be configured to compare the phone numberof a node with phone numbers of other nodes belonging to the samecompany or branch/office. Corporations generally will assign phonenumbers to employees that are similar to one another, for instance, allthe numbers of the corporation can be 617-550-XXXX. As such, the nodeprofile manager 220 can categorize a phone number as a work number for anode if the phone number starts with 617 550 when at least a thresholdnumber of nodes belonging to the same email domain @example.com alsohave the phone number beginning with 617-550. In some embodiments, thethreshold number can be 2, 3, 4, 5, or more. In some embodiments, thethreshold number can be based on a percentage of another value, such asa total number of nodes belonging to the same domain and also having thephone number beginning with the same subset of digits. Conversely, thenode profile manager 220 can be configured to categorize a phone numberas a personal number if the phone number starts with a different set ofnumbers. It should be appreciated that more broadly, the node profilemanager 220 can be configured to extract a regex pattern or specifictemplate of numbers by comparing the phone numbers of multiple nodeprofiles corresponding to the same corporation.

In some embodiments, the node profile manager 220 can be configured tocompare a location of a person with an area code of a phone numberassociated with the person to determine if a phone number is to beclassified as a work phone number or a personal phone number. If theperson lives in the same area as the company's office, the person'spersonal phone number can have similar first few digits as the company'sgeneral phone number. In some such embodiments, the node profile manager220 can be configured to negate the similar digits between the person'sphone number and the company's assigned phone numbers to determine ifthe number identified in the node profile or to be included in the nodeprofile is to be classified as a work phone number or a personal phonenumber. If the person lives in an area that is further away from thecompany based on existing information in the node profile, the nodeprofile manager 220 can be configured to classify a number similar tothe company's general phone number or having an area code correspondingto an area where the company is located as a work phone number. If theperson lives in an area close to the company, the node profile manager220 can be configured to identify the digits of the phone number thatmatch the company's general phone number and use the remaining digits todetermine if the number corresponds to a work phone number or a personalphone number of the person.

If the person lives far away from their work address, the node profilemanager 220 can be configured to reduce the likelihood of assigning, asa personal phone number, a phone number that has an area codecorresponding to the person's work address. More generally, the nodeprofile manager 220 can be configured to rely on additional fields todetermine if a particular number belongs to a work phone number or apersonal phone number of the person.

As described herein, the node profile manager 220 can be configured toused information from node profiles to predict other values. Inparticular, there is significant interplay between dependent fields suchas phone numbers and addresses, and titles and companies, in addition toemail addresses and names, among others.

G. Electronic Activity Tagging

The tagging engine 265 can be any script, file, program, application,set of instructions, or computer-executable code that is configured toenable a computing device on which the tagging engine 265 is executed toperform one or more functions of the tagging engine 265 describedherein.

The tagging engine 265 can use information identified, generated orotherwise made available by the electronic activity parser 210. Thetagging engine can be configured to assign tags to electronicactivities, node profiles, systems of record, among others. By havingtags assigned to electronic activities, node profiles, records ingestedfrom one or more systems of record, among others, the node graphgeneration system 200 can be configured to better utilize the electronicactivities to more accurately identify nodes, and determine types andstrengths of connections between nodes, among others. In someembodiments, the tagging engine 265 can be configured to assign aconfidence score to one or more tags assigned by the tagging engine. Thetagging engine 265 can periodically update a confidence score asadditional electronic activities are ingested, re-ingested and analyzed.Additional details about some of the types of tags are provided herein.A tag can be one or more bits that can be used by the system to label

The tagging engine 265 can assign one or more tags to electronicactivities. The tagging engine 265 can determine, for each electronicactivity, a type of electronic activity. Types of electronic activitiescan include meetings, electronic messages, and phone calls. For meetingsand electronic messages such as emails, the tagging engine 265 canfurther determine if the meeting or electronic message is internal orexternal and can assign an internal tag to meetings or emails identifiedas internal or an external tag to meetings and emails identified asexternal. Internal meetings or emails may be identified as internal ifeach of the participants or parties included in the meeting or emailsbelong to the same company as the sender of the email or host of themeeting. The tagging engine 265 can determine this by parsing the emailaddresses of the participants and determining that the domain of theemail addresses map to the domain name or an array of domain names,belonging to the same company or entity. In some embodiments, thetagging engine 265 can determine if the electronic activity is internalby parsing the email addresses of the participants and determining thatthe domain of the email addresses map to the same company or entityafter removing common (and sometimes free) mail service domains, such asgmail.com and yahoo.com, among others. The tagging engine 265 may applysome additional logic to determine if all emails belong to the sameentity and use additional rules for determining if an electronicactivity is determined to be internal or external. The tagging engine265 can also identify each of the participants and determine whether arespective node profile of each of the participants is linked to thesame organization. In some embodiments, the tagging engine 265 candetermine if the node profiles of the participants are linked to acommon group node (such as the organization's node) to determine if theelectronic activity is internal. For phone calls, the tagging engine 265may determine the parties to which the phone numbers are either assignedand determine if the parties belong to the same entity or differententities.

In some embodiments, the node graph generation system 200 can beconfigured to generate, maintain and update an array of domain namesthat belong to the same company or entity. The node graph generationsystem 200 may do so by monitoring electronic activities and predictingwhether certain domain names belong to the same entity. The node graphgeneration system 200 can monitor a large number of electronicactivities of an entity and determine multiple email accounts of a firstdomain communicate with multiple email accounts of a second domain in amanner that appears to be internal communications. In some embodiments,the node graph generation system 200 can automatically identify allpossible domain names of the company based on a frequency ofcommunications that look like internal communications between identifiedmembers of a company name, the fact that in multiple systems of recordmajority of the communicating node profiles belong to the same orrelated company profile, or by a similarity of the ending part of domainnames, for example “us.ibm.com” and “us.ibm.com”. Electronic activitiescan appear to be internal communications based on analyzing the wordsused in emails, the meeting numbers used in meeting and calendarinvites, as well as determining if the email addresses match certainregex rules that may indicate that the domain names belong to the samecompany. For instance, electronic activities include email addresseshaving domain names us.example.com and uk.example.com may increase alikelihood that both us.example.com and uk.example.com appear to belongto the same company, Example. In some embodiments, if there a certainnumber of emails from certain users of us.example.com to other users ofuk.example.com and the emails appear to be internal communications, thenode graph generation system 200 or the node profile manager 220 can beconfigured to update the node profile of the company, Example, toinclude both domain names, us.example.com and uk.example.com. It shouldbe appreciated that the node graph generation system 200 can thenautomatically update other node profiles and tags previously assigned toelectronic activities responsive to determining that two domains belongto the same company. It should further be appreciated that the nodegraph generation system 200 can also automatically update confidencescores of certain values of fields of other node profiles and confidencescores of tags previously assigned to electronic activities responsiveto determining that two domains belong to the same company.

In some embodiments, the tagging engine 265 can assign an internal tagor external tag to an electronic activity by applying certain logic. Forinstance, the tagging engine can determine that the electronic activityis internal if all the domains associated with the electronic activityare internal (or belong to the same domain). In some embodiments, if thetagging engine 265 determines that only some of the domains are internaland one or more domains are personal (i.e. not business external), thenthe tagging engine can be configured to attempt to match the personalemail addresses to nodes and see if those nodes are linked to the samecompany. If the tagging engine fails to match the personal emailaddresses to nodes and see if those nodes are linked to the samecompany, the tagging engine can be configured to tag the electronicactivity as external and may not link the electronic activity to a groupnode belonging to the domain. In some embodiments, if the tagging engine265 determines that some domains of the email addresses included in theelectronic activity are internal and some are business external, thetagging engine 265 can be configured to link the electronic activity tothe group node corresponding to the external company, and furtherdetermine if individual nodes matching the email address (or first andlast names) exist, and if so, linking the electronic activity with therespective individual nodes. In the event that the tagging engine 265cannot identify an individual node that matches the email address orfirst and last names, the system 200 can create new individual nodesbased on the respective email address or first and last names that wereused to unsuccessfully identify the individual node. In the event thatno individual (people) or group (company) nodes match, and the domaincorresponding to the electronic activity doesn't belong to the list offree/public domains like @gmail then the system 200 can be configured toautomatically create a new group (company) node or generate a flag ornotification for an administrator to take an action.

The tagging engine 265 can further assign a sent tag to emails that aresent by a node associated with the data source provider from which theelectronic activity was received or a received tag to emails that arereceived by a node associated with the data source provider from whichthe electronic activity was received.

In addition, the tagging engine can be configured to assign an inboundtag to received electronic activities corresponding to meetinginvitations and assign an outbound tag to electronic activitiescorresponding to meeting invitations transmitted to other people.Moreover, meetings can be tagged with additional tags, such as a“future” tag when a meeting is scheduled for a time in the future. The“future” tag is subsequently replaced with a “past” tag once the time atwhich the meeting is scheduled to occur is in the past. Moreover, thetagging engine 265 can further assign tags indicating if the meetingtook place or not based on other signals, such as electronic activitiesexchanged within a predetermined time frame of the scheduled meetingtime as described herein or containing written confirmations that themeeting took place or not, such as follow-up notes between participantsor cancellation notice emails. For electronic activities identified asmeetings, the tagging engine 265 can further assign a tag identifying ifthe meeting is in person or if the meeting is a conference call. In someembodiments, the tagging engine 265 can employ a meeting type policy todetermine the type of meeting. In some embodiments, the policy caninclude rules for parsing the location portion or body of a meeting todetermine the location. If the location identifies a physical address ora room or if one of the participants included in the email is anon-human participant associated with a meeting room or other type ofrooms, the tagging engine 265 can determine that the electronic activityis an in-person meeting and can assign an in-person meeting tagindicating that the meeting is an in-person meeting. In someembodiments, an in-person tag can be assigned to the electronic activityand a confidence score can be determined for the in-person tag that isassigned.

The confidence score associated with the in-person tag can be indicativeof a likelihood that the meeting is actually an in-person meeting. Thetagging engine 265 can further be configured to assign an occurrence tagthat can be used to indicate a likelihood that the meeting occurred. Thetagging engine 265 can further be configured to assign a respectiveparticipant attendance tag for each participant that attended themeeting.

To determine the confidence score associated with the in-person tag, thenode graph generation system 200 can scan or analyze electronicactivities associated with the participants of the meeting (and in someembodiments, the electronic activities of all users of the system 200)to identify receipts or other electronic activity, communications, amongothers indicative of the user physically going to the meeting. In someembodiments, the system 200 can scan electronic activities to findflight information, transportation receipts, and ride-sharing receipts,which may include information that would indicate the user physicallygoing to the location associated with the meeting. For instance, if themeeting is at 100 Main St, San Francisco, Calif. on a certain date,electronic activities from an airline identifying a local airport may beused to increase the confidence score of the in-person tag. Similarly,even a flight cancellation receipt may increase the confidence score ofthe in-person tag. This is because even though the person may not haveattended the meeting, the proof that a flight was reserved indicatesthat the meeting was intended to be an in-person meeting. The occurrencetag, which indicates whether the meeting actually occurred, can have itsown confidence score. The greater the confidence score of the occurrencetag, the more likely the meeting occurred. As such, a flightconfirmation email may increase the confidence score of the occurrencetag, while a flight cancellation email may conversely, decrease theconfidence score of the occurrence tag. If multiple participants receiveflight cancellation emails, the system may decrease the confidence scoreof the occurrence tag as it may be indicative of the meeting beingcanceled. However, if multiple participants received flight reservationemails and only a subset of the participants received flightcancellation emails, the system may not decrease the confidence score ofthe occurrence tag by the same amount as the system may assume that themeeting is still occurring but only the subset of participants are notattending. In such cases, the system may decrease the confidence scoreof the participant attendance tag for those participants that receivedflight cancellation emails. Moreover, the system can detect and parse anelectronic receipt from a ride sharing service identifying one of theaddresses as or near the meeting location (for example, 100 Main St, SanFrancisco, Calif.) and use the electronic activity to further increasethe confidence score of the in-person meeting tag as well as theoccurrence tag and the participant attendance tag.

On the other hand, the tagging engine 265 can determine that the meetingis a conference call by applying the meeting type policy and determiningif a phone number or dial-in instructions are provided in the electronicactivity. Furthermore, the tagging engine 265 may receive informationfrom other engines or modules of the system to determine if participantsare in close proximity to one another, based on time zone and locationestimation algorithms used to predict a location of a node as well asdetermine or predict the locations of the participants based onelectronic activities that occur within a predetermined time window ofthe meeting time that involve the participants. Some of the rules relyon determining a predicted work schedule of the node, a predictedlocation of the node, and inferred behavior before and after the meetingthat can be determined from other electronic activities.

In some embodiments, the tagging engine 265 or the system 200 can beconfigured to cause the system 200 to initiate a call to a phone numberincluded in a meeting invite and responsive to joining the meeting,identify one or more participants of the meeting for instance, based onidentifying the phone number from which each of the participants iscalling in and comparing those phone numbers to the data in the nodegraph or node profiles used to generate the node graph, convertingspeech to text, voice recognition, voice footprinting, among others. Insome embodiments, the tagging engine can determine the participants whoattended the meeting based on the attendees that accessed a link to aweb session and in some such embodiments, used their email address tolog into the web session. In some embodiments, the tagging engine 265can determine what time a participant joined, a level of contribution ofthe participant during the meeting, how long the participant attendedthe meeting for, and generate one or more additional tags based on oneor more of the participants' involvement.

As described above with respect to in-person meetings, the taggingengine 265 can also provide occurrence tags for conference call orvirtual meetings as well as attendance tags for participants of suchmeetings. The occurrence tags can have respective confidence scoresindicating the likelihood that the meeting actually occurred. Similarly,the participant attendance tags can be assigned to participants and canhave respective confidence scores indicating the likelihood that theparticipant actually attended the meeting. The confidence scores of theoccurrence tags and the attendance tags can be determined based onelectronic activities that reference the meeting. In some embodiments,an electronic activity representing a phone log of a users phone dialinginto to a meeting number can be used to increase the confidence score ofthe occurrence tag of the meeting as well as the confidence score of theattendance tag.

The tagging engine 265 can further be configured to assign tags topeople identified or included in one or more electronic activities.These tags can identify a role of the person included in the electronicactivity. The tags can include a sender tag indicating a participant asa sender of the electronic activity or an organizer tag indicating aparticipant as an organizer of a meeting. Other similar types of tagscan be assigned to participants based on whether they are included inthe To line, the CC line or the BCC line. The tagging engine 265 canfurther be configured to tag participants based on the context of theelectronic activity. For instance, if the electronic activity isdetermined to be associated with an opportunity, the tagging engine canassign tags to various participants, including tags indicating who thebuyer is, who the seller is, who the decision maker is, who the championis, among others. This information can be determined based on nodeprofiles of the participants, their level of involvement in theelectronic activity or the opportunity in general, among others. Thetags can be assigned with certain confidence scores. As additionalelectronic activities are processed, the confidence scores of these tagscan increase or decrease.

In some embodiments, natural language processing can be used to parseelectronic activities exchanged between the participants to determinethe type of meeting. For instance, an electronic activity exchangedafter the meeting may indicate a phrase such as “Thanks for the lunch”which may indicate that the meeting was an in-person meeting, amongothers. In some embodiments, the tagging engine 265 can further tagelectronic activities, such as meetings, with tags indicating if themeeting actually took place. As described above, the tagging engine 265can tag a meeting as having taken place responsive to identifying asubsequent electronic message that included a phrase such as “Thanks forthe lunch.” In some embodiments, the tagging engine can determine thatthe meeting is an in-person meeting by detecting an address or physicallocation in the body or location fields of the electronic activity. Thetagging engine can further attribute a confidence score to the tag basedon various data points the tagging engine relies on to determine thatthe electronic activity corresponds to an in-person meeting. Theconfidence score of the tag can increase or decrease based on additionalelectronic activity parsed by the system. For instance, electronicactivity exchanged between the participants that may include variousphrases that are detected via natural language processing, for instance,“great seeing you,” or “thanks for lunch” can increase the confidencescore of the in-person tag indicating that the meeting is an in-personmeeting. In addition, the electronic activity exchanged between theparticipants can increase the confidence score of the participantattendance tags of the sender and recipient of the email. Similarly,electronic activities including receipts of transportation (forinstance, UBER/LYFT/flight receipts) to or from the physical locationassociated with the meeting may be used to increase the confidence scoreof the in-person tag assigned to the meeting, the occurrence tagassigned to the meeting and the participant attendance tag assigned torespective participants of the meeting. Additional details regardingtagging electronic activity are provided herein.

The tagging engine 265 can further assign tags indicating if an email isa blast email. In some embodiments, the tagging engine 265 can determineif an email is a blast email by parsing the message header of the email,identifying a message identifier field of the email and extracting thevalue of the message identifier field. The tagging engine can thencompare the value of the message identifier field and compare the valueto values of other electronic activities to determine if the valuespartially match. Furthermore, the tagging engine 265 can compare thewords included in the body or subject line of the electronic activitiesthat at least partially match and if the ratio of similar words todifferent words exceeds a threshold, the tagging engine 265 candetermine if the email is a blast email. In some embodiments, thetagging engine 265 can determine electronic activities corresponding toa blast email by analyzing multiple electronic activities andidentifying a subset of the multiple electronic activities as blastemails responsive to determining that each electronic activity of thesubset has a low variability of word count relative to the otherelectronic activities in the subset and a low variability in a languagecomplexity index relative to the other electronic activities in thesubset.

In some embodiments, other signals may be used to determine if the emailis a blast email, for instance, a time at which the emails were sent,and if a similar email was previously sent to a large number of people.In some embodiments, the tagging engine 265 can assign a blast email tagto an instant electronic activity responsive to determining that asimilar electronic activity that is similar to the instant electronicactivity above a predetermined similarity threshold was associated to alarge number of nodes in a node storage database maintained by thesystem 200. In certain embodiments, the tagging engine 265 can learnfrom previously tagged electronic activities known to be blast emailsand use the learnings from such electronic activities to assign a tag toan instant email having language that is similar above a predeterminedsimilarity threshold to one or more electronic activities previouslytagged as blast emails. By determining if an email is a blast email,effort estimation can be more accurately computed.

The tagging engine 265 can further assign tags indicating if an email isa cold email. In some embodiments, the tagging engine 265 can determineif an email is a cold email by applying natural language processing toidentify patterns or signals that may indicate that the email is a coldemail or by determining a tone of an email. In some embodiments, thetagging engine 265 may determine that an email is a cold email if theparticipants of the email have not exchanged any electronic activity inthe past. In some embodiments, the tagging engine 265 may determine thatan email sent from a sender to a recipient is a cold email if therecipient of the email has not previously transmitted a response to anyelectronic activity sent from the sender to the recipient in the past.In some embodiments, even if the recipient of the email has notpreviously transmitted a response to any electronic activity sent fromthe sender to the recipient in the past the tagging engine 265 maydetermine that an email sent from a sender to a recipient is not a coldemail if the recipient and the sender have communicated via other formsof communication or via other email addresses associated with arespective node of the sender or recipient in the past. In this way, ifthe recipient starts a new job and gets a new email address, electronicactivities sent to the new email address by a sender who has previouslycommunicated with the recipient at the old job would not be classifiedor tagged as a cold emails because the node graph would indicate thatthe sender has communicated with the recipient in the past albeit via adifferent email address of the recipient that is determined based on thevalues of email addresses stored in a node profile of the recipient. Insome embodiments, the tagging engine 265 can determine if an email is acold email based on a number of cold emails the sender has sent in thepast to one or more recipients as well as by looking at the node graphto determine a number of nodes with which the sender and recipient arecommonly connected.

The tagging engine 265 can further assign tags indicating aclassification of the electronic activity based on the participantsincluded in the electronic activity. For instance, if one of theparticipants is a lawyer, the tagging engine 265 can assign a tagindicating that the electronic activity relates to legal. Moreover, thetagging engine 265 can further assign tags indicating a classificationof the electronic activity based on the subject matter included in theelectronic activity. The tagging engine 265 can determine a subjectmatter based on natural language processing, keywords, regex patterns orother rules that may be used to determine the subject matter. In someembodiments, filtering policies that may be provided or configured byusers, companies, accounts, among others, may be used by the taggingengine 265 to assign one or more tags. Such tags can be used forfiltering, matching electronic activities to record objects of systemsof record, determining if emails are personal or business related, amongothers.

In some embodiments, the tagging engine 265 can be configured todetermine if an electronic activity is a personal electronic activity orif it is a business related electronic activity. In some embodiments,the tagging engine 265 can determine that an electronic activity ispersonal based on parsing the contents of the electronic activity. Insome embodiments, the tagging engine 265 can determine that theelectronic activity is personal if the electronic activity is sentduring non-work hours and the context of the electronic activity isunrelated to work. In some embodiments, the tagging engine 265 candetermine that the electronic activity is personal if the participantsof the electronic activity have titles or job functions that typicallydo not overlap or correspond to companies that do not generally engagein work related activities. In some embodiments, the tagging engine 265can also evaluate various features, characteristics or values of fieldsof node profiles of the participants of the electronic activity todetermine whether the electronic activity is personal. For instance, thetagging engine 265 may determine that the electronic activity is likelyto be personal if the participants of the electronic activity have thesame last name, as derived from the header of the electronic activity,the body or contents of the electronic activity, a signature included inthe electronic activity or from the node profiles of the participants ofthe electronic activity. It should be appreciated that the taggingengine 265 may not need to rely on information stored in a node profileof a participant of the electronic activity to determine if theelectronic activity is personal. For example, the tagging engine 265 candetermine if the participants share the same last name by parsing theheader of the electronic activity, the body or contents of theelectronic activity, a signature included in the electronic activity.Further, if the participants have previously communicated with oneanother using their personal email addresses or if the contents of theelectronic activity suggest that they have a prior relationship outsideof work, the tagging engine 265 can determine that the participants maybe related outside of work and may be configured to determine that theelectronic activities exchanged between them are personal electronicactivities. The tagging engine 265 can be configured to tag suchelectronic activities with a personal tag indicating that the electronicactivity is determined to be personal. As described herein, the taggingengine 265 or the system, in general, can assign a confidence score tothe tag based on how confident the system believes the electronicactivity is personal (or on-work related) in nature, based on a numberof methods, described above.

In some embodiments, the system 200 or the node profile manager 220 canbe configured to determine that two node profiles have a personal(non-professional) relationship either based on the electronicactivities exchanged between them that may be tagged with a personaltag. The system can then tag the two node profiles as having a personalrelationship. The system can further determine a confidence score forthe tag classifying the two node profiles based on how confident thesystem is in its prediction that the two node profiles have a personalrelationship. In some embodiments, the system 200 or the node profilemanager 220 can further determine if two nodes have a personalrelationship based on commonalities in values in their node profiles,for instance, their home addresses (if they are neighbors), college orschool affiliations (alumni/classmates), same last names, othernon-professional affiliations, or other signals that may indicate thetwo node profiles may have a personal relationship.

The system 200 or the tagging engine 265 can be configured to use thepersonal tag between the node profiles to classify subsequent electronicactivities exchanged between the node profiles. In some embodiments, asdescribed below, the system can be configured to restrict matchingelectronic activities with a personal tag to record objects. The systemcan further be configured to either unmatch or unlink previously matchedelectronic activities from record objects of systems of record or removesuch activities from existing data structures.

It should be appreciated that the system can conversely or similarlydetermine that certain electronic activities are professional in natureand tag such electronic activities with a professional tag. The system200 can also be configured to determine that relationships between nodeprofiles may also be professional based on their respective nodeprofiles as well as past electronic activities exchanged between them.

It also should be appreciated that the system 200 or the tagging engine265 can conversely or similarly determine that certain electronicactivities can be more professional in nature. In some embodiments, thetagging engine 265 can determine that an electronic activity isprofessional if the content of the electronic activity relates to sales,recruiting, scheduling an appointment or other business relatedactivities. The tagging engine 265 can then assign a professional tag tosuch an electronic activity indicating that the electronic activity isprofessional in nature. The tagging engine 265 can further assign a tagindicating that the electronic activity is relating to sales, recruitingor scheduling an appointment based on the context of the electronicactivity. Such tags can be used to determine whether or not to match theelectronic activity to a record object of a system of record. Forinstance, if the electronic activity relates to sales, the system 200can tag the electronic activity with a sales tag, which the system 200can use to determine to match the electronic activity to a record objectof one or more systems of record as a sales related electronic activitycan be a useful data point for a company in evaluating various aspectsof their business processes. In another example, electronic activitiesrelating to scheduling can be provided a scheduling tag, which can beused by the system 200 to filter out or restrict such electronicactivities from being matched to record objects. Restricting certainelectronic activities from being matched to record objects reduces thecomputing resources required for matching electronic activities torecord objects by reducing the total volume of electronic activities tomatch. Restricting certain electronic activities from being matched torecord objects also reduces the amount of noise in systems of record asscheduling related electronic activities add noise to the system ofrecord.

It should be appreciated that certain tags, such as scheduling tags canbe used to filter out electronic activities from a queue of electronicactivities that the system 200 may attempt to match to record objects.Other such types of tags may include personal tags indicating that theelectronic activity is personal, internal tags indicating that theelectronic activity as internal to a company, among others.

The tagging engine 265 can further identify certain types of electronicactivities that may enhance the generation of the node graph or furtherdefine roles of nodes. For instance, in an out of office email response,a person may identify a second person to contact in their absence. Thetagging engine 265 can tag the electronic activity as an out of officeresponse but further allow the node profile manager 220 to update thenode profile of the nodes to indicate the potential relationship betweenthe person who is out of office and the second person to contact intheir absence or create a new node profile for that person if such anode profile doesn't yet exist.

The tagging engine 265 can assign additional tags, such as vacation tagsthat can be used by the node profile manager 220 to update the nodeprofile of the node accordingly. The tagging engine 265 can assign avacation tag to an electronic activity responsive to determining thatthe electronic activity corresponds to the person being on vacation. Thenode profile manager 220 can parse the timing of the vacation from theelectronic activity and update the node profile of the person onvacation. This information can then be passed to one or more systems ofrecord and cause the systems of record to update their settings for thegiven person.

In addition, the tagging engine 265 can be configured to assign a ‘nolonger with company’ tag to an electronic activity responsive to parsingthe electronic activity. This information can then be passed to one ormore systems of record and cause the systems of record to update theirsettings for the given person. In addition, the ‘no longer with company’tag can cause the system 200 to stop future emails to be sent to theperson, and also trigger the system 200 to determine which company thatperson joined.

In some embodiments, the tagging engine 265 ca be configured to assign a‘parental leave’ tag to an electronic activity responsive to parsing theelectronic activity. The parental leave tag can be helpful to predictwhen a person may be returning to work. In addition, the system 200 canassign a parental leave tag to a node profile and further associate thenode profile to one or more other nodes or persons that have beenidentified as taking over the responsibilities of the person on parentalleave.

In some embodiments, the tagging engine 265 can tag an electronicactivity with a deceased tag responsive to parsing the electronicactivity. In some embodiments, the system 200 can then update theassociated node profile indicating that the person is deceased.

In some embodiments, the tagging engine 265 can identify a uniqueelectronic activity identifier for the electronic activity and generatea plurality of tags to assign to the electronic activity. The taggingengine 265 can generate tags to indicate if the electronic activity isexternal or internal, the participants associated with the electronicactivity, an amount of time to generate or perform the electronicactivity, job titles or seniority levels of the participants based ontheir job titles, departments in the organization, to which participantsmay belong based on their job titles, any values, opportunities orrecord objects with which the electronic activity may be linked orotherwise associated, one or more stages of the sales opportunity or anyother system of record process, among others.

The tagging engine 265 can be configured to assign custom tags based onone or more tagging policies of one or more users or subscribers of thesystem 200. For instance, a subscriber of the node graph generationsystem 200 may desire to generate custom tags that allows the subscriberto tag all electronic activity including ride sharing receipts thatidentify the company's address. The subscriber may choose to then usethese tags to identify all electronic activity that include ride sharingreceipts that identify the company's address to gather information aboutthe employees' use of ride sharing to and from work. The subscriber canuse the information to improve business processes, such as consideringproviding a shuttle service to employees or negotiating with a ridesharing company for discounted pricing. The tagging engine 265 canprovide a subscriber an interface through which subscribers can definepolicies for assigning such custom tags.

It should be appreciated that custom tags can be defined using one ormore pieces of information from electronic activities. For instance,custom tags can be defined for certain email addresses, certain names,certain combination of senders and recipients, as well as based onwords, phrases or other content included in the subject line or body ofan electronic activity. For instance, emails that include“legal@example.com” can be tagged as Legal. Emails that mention “cell”or “mobile” and a regex pattern that matches a cell phone number in thebody of an email but not part of the signature block of the email can betagged as Cell. Emails that include a regex pattern that matches asocial security number in the body of an email can be tagged as socialsecurity number, while emails that include a regex pattern that matchesa credit card number in the body of an email can be tagged as creditcard number. The tagging engine 265, the filtering engine 270 or thenode graph generation system 200 can then use these tags to process theelectronic activities tagged with these tags in accordance to one ormore processing policies, such as filtering policies described herein.The filtering policies can also be customized for a given user, companyor subscriber of the system 200 such that a company can deploy rules tohandle such emails in accordance with the company's specific rules.

The tagging engine 265 may iteratively tag and re-tag the sameelectronic activities as more information is received. The taggingengine can be configured to recalculate, re-ingest and re-featurize, andre-tag all data associated with electronic activities to further refinethe tags.

The tagging engine 265 can tag electronic activities based on contextderived from features of such electronic activities. As described above,the tagging engine 265 can assign tags indicating a type of meeting:in-person vs. conference call; internal vs. external, a location of theparticipants to determine if the meeting is an in-person meeting, a timezone of the meeting, countries associated with participants of themeetings, among others.

In some embodiments, the tagging engine 265 can identify if the meetingis a conference call or a web-based meeting. In some embodiments, thetype of activity can determine the types of tags to assign to theactivity. For instance, for meetings, the tagging engine 265 can assignthe following tags: External, internal, in-person, conference call, andcustom tags, based on NLP, regex and other rules, customized by theuser. For emails, the tagging engine 265 can assign the following tags:External, internal, sent, received, blast, cold. In some embodiments,blast detection techniques can be used to determine if the email is ablast email. These techniques include natural language processinganalysis, blast email header analysis, volume of electronic activity fora given node, as well as MIME message data. Generally, blast emails donot include a Blast Message ID that is common across all of the blastemails. As such, detecting an email as a blast email is quite complex.In fact, blast emails are generally generated to appear as non-blastemails and as such, the present disclosure provides techniques that arebased on the low variability of language complexity and word count. Insome embodiments, the blast email tag assigned can include metadataidentifying, for instance, the number of emails in a blast, the toolused to send the blast. The blast email tag can be used to group allemails of the blast and can include metadata about the group of emails.The tagging engine can deploy artificial intelligence to stitch theblast message ID together across multiple emails to identify if aportion of a message ID is common across multiple emails. For calls, thetagging engine 265 can assign tags to the call indicating if the callwas electronically logged or manually entered. The call can be taggedbased on the caller and the receiver, duration, disposition, etc.

In some embodiments, the tagging engine 265 can employ custom policiesfor tagging electronic activities. For instance, the tagging engine cantag every first meeting with a company as a new business meeting. Thetagging engine can tag every meeting with a CXO title, such as CEO, CMO,COO, CLO, CFO, CSO, as CXO. The tagging engine can tag every meetingwith CFO as finance. A reporting engine can then use these tags togenerate custom reports for instance, a report identifying all newbusiness meetings, or all activities involving finance, among others.

Tags can also be assigned for certain words, such as product names,taglines, competitor mentions, among others. By parsing emails ofemployees to identify the use of certain words or phrases specificallydefined for a particular entity, the tagging engine can tag suchelectronic activities to particular products and use such electronicactivities to determine if training is needed, if the correct messagingis being used or if the employees are implementing the latest messagingoutlined by the company. For instance, a company can train reps to sayX, but then train reps to say Y, and then use tags (from NLP) todetermine which reps actually say Y. For example, if a company has18,000 sales reps, how does the company ensure their employees are usingthe new training or actively selling a new product. In addition, thetagging engine 265 can apply policies to tag electronic activities basedon a sentiment analysis. For instance, the tagging engine 265 can applyemployee activities tags based on, negative or positive sentiment withthe mention of the company's competitor or the company's feature.

In some embodiments, the tagging engine 265 can assign tags based onpredicting likelihood of deal or business process completion and time tocompletion from electronic activities. Additional details regarding howthis is determined is described herein and based in part on stageclassification and the roles of the participants in the electronicactivities.

In some embodiments, tags can be defined by rules. Some rules can beglobal rules, company rules defined by company, team level rules anduser level rules.

H. Filtering Engine

The filtering engine 270 can be any script, file, program, application,set of instructions, or computer-executable code that is configured toenable a computing device on which the filtering engine 270 is executedto perform one or more functions of the filtering engine 270 describedherein.

The filtering engine 270 can use information identified, generated orotherwise made available by the tagging engine 265. The filtering engine270 can be configured to block, remove, redact, delete, or authorizeelectronic activities tagged or otherwise parsed or processed by thetagging engine 265. For example, the tagging engine 265 can beconfigured to assign tags to electronic activities, node profiles,systems of record 9360, among others. The filtering engine 270 can beconfigured with a policy or rule that prevents ingestion of anelectronic activity having a specific tag or any combination of tags,such as a credit card tag or social security tag. By applying filteringrules or policies to tags assigned to electronic activities, nodeprofiles, or records from the one or more systems of record, amongothers, the node graph generation system 200 can be configured to block,delete, redact or authorize electronic activities at the ingestion stepor redact out parts or whole values of any of the fields in the ingestedelectronic activities. Additional details about some of the types offiltering based on tags are provided herein.

I. Source Health Scores Including Field-Specific Health Scores, OverallHealth Scores and Determining Trust Scores Based on Health Scores

The source health scorer 215 can be any script, file, program,application, set of instructions, or computer-executable code that isconfigured to enable a computing device on which the source healthscorer 215 is executed to perform one or more functions of the sourcehealth scorer 215 described herein. The source health scorer 215 isconfigured to access a system of record and retrieve all data stored inthe system of record. The source health scorer 215 can then identifyeach record object stored in the system of record and determine, foreach record object, a number of missing values of fields. The sourcehealth scorer can then generate a field-specific score for each fieldindicating a health or quality of each field of the system of record.The source health scorer 215 can further determine an overall healthscore for the source based on the field-specific scores of each field.In some such embodiments, the overall health score is based on missingfield values.

The source health scorer 215 can further be configured to determine ifthe values of fields of record objects are accurate by comparing thevalues to node profiles maintained by the node profile manager 220 or torecord objects maintained by the record objects manager. Based on thenumber of values that are inconsistent with the values maintained by thenode graph generation system 200, the source health scorer can generatea health score for the system of record.

The source health scorer 215 can similarly generate a health score foreach system of record. The source health scorer 215 can then compare thehealth score of a given system of record to the aggregate health scoresof a plurality of systems of record to determine a relative trust scoreof the system of record. In some embodiments, the source health scorer215 can assign different weights or scores to different types of systemsof record. The source health scorer 215 may assign lower health scoresto data included in a system of record that is generated using manualentry relative to node profiles that are automatically populated orgenerated by the node graph generation system 200 based on electronicactivities.

Further, different types of sources can include emails, or emailsignatures within an email, one or more systems of record, among manyother source types. The trust score of a source can be determined basedon the health score of the source, at least in the case of a system ofrecord. In some embodiments, the trust score assigned to electronicactivity such as an email can be greater than a trust score assigned toa data point derived from a system of record as the system of record canbe manually updated and changed. Additional details regarding the healthscore of a system of record are described below.

In some embodiments, the health score of a system of record maintainedby a data source provider can be determined by comparing the recordobjects of the system of record with data that the system has identifiedas being true. For instance, the system 200 can identify, based onconfidence scores of values (as described below) of fields, that certainvalues of fields are true. For instance, the system may determine that avalue is true or correct if multiple data points provide support for thesame value. In some embodiments, the multiple data points may forexample, be at least 5 data points, at least 10 data points, or more.The system 200 can then, for a value of a field of a record object ofthe system of record, compare the value of the system of record to thevalue known to the system to be true. The system can repeat this foreach field of a record object to determine if any values of a recordobject are different from the values the system knows to be true. Insome embodiments, when determining the health score, the system may onlycompare those values of fields of record objects of the system of recordthat the system has a corresponding value that the system knows is true.For instance, the system may know that a phone number of a person “RogerNadal” is 617-555-3131 and may identify such a number as true based onmultiple data points. However, the system may not know an address of theperson Roger Nadal. In such an instance, the system may only compare thephone number of the record object corresponding to Roger Nadal todetermine the health score of the system of record but not compare theaddress of the person Roger Nadal as the system does not know theaddress of Roger Nadal. Furthermore, even if the node profile of RogerNadal had an address but the confidence score of the address was below apredetermined threshold, the system would not compare the address fromthe system of record to the address of the node profile since the systemdoes not have enough confidence or certainty that the address is true.As such, the system can be configured to determine the health score of asystem of record by comparing certain values of record objects of thesystem of record to values the system knows as true or above apredetermined confidence score. In this way, in some embodiments, thehealth score of the system of record is based on an accuracy of the dataincluded in the system of record rather than how complete the system ofrecord is not.

As described above, the health score of a system of record can be anoverall health score that can be based on aggregating individualfield-specific health scores of the system of record. It should beappreciated that the system 200 can assign different weights to each ofthe field-specific health scores based on a volume of data correspondingto the respective field, a number of values that does not match valuesthe system 200 knows to be true, among others.

In certain situations, the system 200 can compute trust scores for datapoints based on the health score of a system of record. In someembodiments, the system 200 can compute the trust score based on theoverall health score of the system of record that is the source of thedata point. However, in some embodiments, it may be desirable toconfigure the system 200 to provide more granularity when assigning atrust score to a system of record that is the source of the data point.For instance, a company may meticulously maintain phone numbers ofrecord objects but may not be so meticulous in maintaining job titles ofrecord objects such that the field specific health score for the phonenumber field of the system of record is much better than thefield-specific health score for the job title field and also better thanthe overall health score of the system of record determined based on theaggregate of the respective field specific health scores of fields ofthe system of record. In some embodiments, as will be described herein,if a data point supporting a phone number of a node profile is providedby the system of record, the system 200 may be configured to determine atrust score for the data point based on the field specific health scoreof the field “phone number” for the system of record rather than theoverall health score of the system of record, which is lower because thefield specific health score of the field “job title” of the system ofrecord is much lower than the field specific health score of the field“phone number.” By determining trust scores based on the field-specifichealth scores of systems of record, the system 200 may be able to moreaccurately rely on the data point and provide a more accuratecontribution score of the data point as will be described herein.Additional concepts relating to health scores and trust scores areprovided herein with respect to section 5 relating to monitoring healthscores of systems of record.

J. Node Field Value Confidence Scoring

The attribute value confidence scorer 235 can be any script, file,program, application, set of instructions, or computer-executable code,that is configured to enable a computing device on which the attributevalue confidence scorer 235 is executed to perform one or more functionsof the attribute value confidence scorer 235 described herein. Theattribute value confidence scorer 235 can be configured to determine aconfidence of each value of an attribute of a node profile. Theconfidence of a value is determined based in part on a number ofelectronic activities or sources that contribute to the value, timesince each electronic activity provided support or evidence of thevalue, time since the field value in the source system of record waslast modified or confirmed by a human operator, as well as the source ofthe electronic activity. Electronic activity that is received from mailservers or another source that does not involve manual entry may beassigned a greater weight (or trust/health score) than a source thatinvolves manual entry, such as a customer relationship management tool.

The attribute value confidence scorer 235 can be configured to determinea confidence of each value of an attribute of a node profile. Anattribute or field can have multiple candidate values and the value withthe highest confidence score can be used by the node graph generationsystem for confirming or validating the value of the field. Theattribute value confidence scorer 235 can apply one or more scoringalgorithms to determine the likelihood that each value is a correctvalue of the attribute. It should be appreciated that a value does notneed to be current to be correct. In some embodiments, as new entitiesare onboarded into the system, electronic activities and systems ofrecord corresponding to systems of record of the new entities can beprocessed by the system 200. In processing these electronic activitiesand systems of record, some electronic activities can be associated withdates many years in the past. Such electronic activities are notdiscarded. Rather, the system processes such electronic activities andinformation extracted from these electronic activities are used topopulate values of fields of node profiles. Since each data point isassociated with a timestamp, the data point may provide evidence for acertain value even if that value is not a current value. One example ofsuch a value can be a job title of a person. The person many years agomay simply have been an associate at a law firm. However, that person isnow a partner at the firm. If emails sent from this person's emailaccount are processed by the system 200, more recently sent emails willhave a signature of the person indicating he's a partner, while olderemails will have a signature of the person indicating he's an associate.Both values, partner and associate are correct values except onlypartner is the current value for the job title field. A confidence scoreof the current value may be higher in some embodiments as data pointsthat are more recent may be assigned a higher contribution score thandata points that are older. Additional details about contribution scoresand confidence scores are provided below.

In some embodiments, a node profile can correspond to or represent aperson. As will be described later, such node profiles can be referredto as member node profiles. The node profile can be associated with anode profile identifier that uniquely identifies the node profile. Eachnode profile can include a plurality of attributes or fields, such asFirst name, Last name, Email, job title, Phone, LinkedIn URL, Twitterhandle, among others. In some embodiments, a node profile can correspondto a company. As will be described later, such node profiles can bereferred to as group node profiles. The group node profile can besimilar to the member node profile of a person except that certainfields may be different, for example, a member node profile of a personmay include a personal cell phone number while a group node of a companymay not have a personal cell phone number but may instead have a fieldcorresponding to parent company or child company or fields correspondingto CEO, CTO, CFO, among others. As described herein, member nodeprofiles of people and group node profiles of companies for the mostpart function the same and as such, descriptions related to nodeprofiles herein relate to both member node profiles and group nodeprofiles. Each field or attribute can itself be a 3-dimensional array.For instance, the First name attribute can have two values: firstname_1|first name_2, one Last name value and three email address valuesemail_A|email_B|email_C. Each value can have an Occurrence (counter)value, and for each occurrence that contributes to the Occurrence value,there is an associated Source (for example, email or System of record)value and an associated timestamp (for example, today, 3;04 pm PST)value. In this way, in some embodiments, each value of a field orattribute can include a plurality of arrays, each array identifying adata point or an electronic activity, a source of the data point orelectronic activity, a time associated with the data point or electronicactivity, a contribution score of the data point or electronic activityand, in some embodiments, a link to a record of the data point orelectronic activity. It should be appreciated that the data point can bederived from a system of record. Since systems of records can havevarying levels of trust scores, the contribution score of the data pointcan be based on the trust score of the system of record from which thedata point was derived. Stated in another way, in addition to eachattribute being a 3-dimensional array, in some embodiments, each valueof an attribute can be represented as a plurality of arrays. Each arraycan identify an electronic activity that contributed to the value of theattribute, a time associated with the electronic activity and a sourceassociated with the electronic activity. In certain embodiments, thesub-array of occurrences, sources and times can be a fully featuredsub-array of data with linkage to where the data came from.

K. Node Profile Inferences

Certain information about a node can be inferred by the node graphgeneration system 200 based on information included in electronicactivities ingested by the system 200. For instance, the node profilemanager 220 or the electronic activity tagging engine 265 can infer if aperson has left a job or switched jobs if the occurrence counter for afirst value stops increasing or the frequency at which the occurrencesof the first value appear has been reduced and the occurrence counterfor a second value is increasing or the occurrences are more recent orare received from a source that has a higher trust score indicating thatthe person has changed email addresses, which can indicate that theperson has switched jobs. In certain embodiments, the system 200 candetermine if the second value corresponds to an email addresscorresponding to another employer or another company. In someembodiments, the system 200 can determine if the domain name of theemail address corresponds to a list of known domain names correspondingto personal, non-work email addresses (for instance, gmail.com,outlook.com), among others. In some embodiments, the system 200 candetermine if the domain name is associated with a predetermined minimumnumber of accounts with the same domain name. The node profile manager220 can look at relevancy of Source, recency of time and Occurrences todetermine whether to update the email field from the first email(Email_A) to the second email (Email_B).

In some embodiments, the attribute value confidence scorer 235 describedherein can provide mechanisms to confirm validity of data using multipledata sources. For instance, each electronic activity can be a source ofdata. As more electronic activities are ingested and increase theoccurrence of a value of a data field, the system can confirm thevalidity of the value of the field based on the number of occurrences.As such, the system described herein can compute a validity score of avalue of a field of a node profile based on multiple data sources. Forinstance, the system can determine how many data sources indicate thatthe job title of the person is VP sales and can use the health score ofthose sources to compute a validity score or confidence score of thatparticular value. In addition, the timestamp associated with eachelectronic activity can be used to determine the validity score orconfidence score of that particular value. More recent electronicactivities may be given greater weight and therefore may influence thevalidity score of the particular value more than electronic activitythat is much older.

It should be appreciated that electronic activity that is generated andingested in real-time or near real-time can be assigned a greater weightas the electronic activity has no bias, whereas data input manually intoa system of record may have some human bias. In certain embodiments inwhich data is imported from systems of records, the weight the data hason a confidence score of the value is based on a trust score of thesystem of record from which the data is imported.

In some embodiments, the attribute value confidence scorer 235 candetermine a confidence score of a data point based on the data sourcesat any given time. A data point can be a value of a field. For example,“VP, product” can be a value for a job title of a node profile. Theattribute value confidence scorer 235 can utilize the electronicactivities ingested in the system to determine how many electronicactivities have confirmed that the value for the job title is VP,product for that node in the email signatures present in thoseelectronic activities. In some embodiments, the attribute valueconfidence scorer 235 can take into account a recency of the activitydata and the source type or a health score of the source type todetermine the confidence score of the value of the field. In someembodiments, the node profile manager can determine a current value of afield based on the value of the field having the highest confidencescore.

L. Stitching Time Series Together

The system can be configured to maintain a time series array for eachfield of a node profile that can be used to determine a timeline ofevents associated with the node. The system can maintain the time seriesarray based on timestamps of all data sources of all values for eachfield of the node. For instance, the timeline can be used to determine acareer timeline with work history information, a series of job titlechanges indicating promotions, among other things. In addition, thetimeline of events can track a person's movement across companies orgeographic locations over time as well as a list of other nodes orpersons the company has been affiliated or associated with at differentpoints in time. For instance, the job title of a node profile caninclude the following values over a period of time: director|vpsales|president|CEO. In certain embodiments, each of the values of thetitle can have an increase in a confidence score at different times andas a confidence score of a given value of the title field increases, theconfidence score of the preceding value of the title field decreases.

M. Node Connections

The node pairing engine 240 can be any script, file, program,application, set of instructions, or computer-executable code that isconfigured to enable a computing device on which the node pairing engine240 is executed to perform one or more functions of the node pairingengine 240 described herein. The node pairing engine 240 can compute aconnection strength between nodes based on electronic activityassociated with both of the nodes. More of the recent electronicactivity between the two nodes will indicate a greater connectionstrength. Moreover, with different tags assigned to those electronicactivities, the node pairing engine 240 can further determine therelationship between the two nodes and the context in which the twonodes are connected. For instance, two nodes may be connected throughtheir work on one or more opportunities or one node may report to thesecond node, among others. The context behind the relationships can bederived from the electronic activity associated with the two nodes aswell as other electronic activity associated with each node independentof the other node. In certain embodiments, the node pairing engine 240can use metadata from the electronic activities to infer connectionstrength or relationships. For instance, the node pairing engine cancompute an average time a node takes to respond to another node and usethe average time to respond to determine a connection strength. In someembodiments, the average time to respond is inversely proportional tothe strength of the connection. Furthermore, the node pairing engine 240can look at other information relating to the electronic activities toinfer connection strengths. If a node responds to another node outsideof business hours can be an indicator of connection strength orconnection relationships.

The node pairing engine 240 can determine a connection strength betweennodes at a given point in time across a timeline. As the nodes exchangefurther electronic activity, the connection strength can increase. Thesystem is configured to determine the connection strength at aparticular time period by filtering the electronic activities based ontheir respective times. In certain embodiments, the node pairing engine240 can recalculate a connection strength between nodes responsive to atrigger. In some embodiments, the trigger can be based on a confidencescore falling below a predetermined threshold indicating that theconfidence in a particular value is unstable or unusable. For instance,the trigger can be satisfied or actuated when the node pairing engine240 determines that the confidence score of a particular value of afield, such as a current employer of a person is below a predeterminedconfidence score (indicating that the person may no longer be at aparticular company). In certain embodiments, certain changes to valuesin fields can trigger recalculating a connection strength irrespectiveof activity volume, for instance, when a new value under the employerfield is added in the node.

In some embodiments, the node pairing engine 240 can determine aconnection strength between two nodes by identifying each of theelectronic activities that associate the nodes to one another. Incontrast to other systems that may rely on whether a node has previouslyconnected with another node, the node pairing engine 240 can determine aconnection strength at various time periods based on electronicactivities that occur before that time period. In particular, the nodepairing engine 240 can determine staleness between nodes and take thestaleness to determine a current connection strength between nodes. Assuch, the node pairing engine 240 can determine a temporally changingconnection strength. For instance, the node pairing engine 240 candetermine how many interactions recently between the two nodes. The nodepairing engine 240 can determine whether the connection between the twonodes is cold or warm based on a length of time since the two nodes wereinvolved in an electronic activity or an amount of electronic activitybetween the two nodes. For instance, the node pairing engine 240 candetermine that the connection strength between two nodes is cold if thetwo nodes have not interacted for a predetermined amount of time, forinstance a year. In some embodiments, the predetermined amount of timecan vary based on previous electronic activity or past relationships bydetermining additional information from their respective node profiles.For instance, former colleagues at a company may not have a coldconnection strength even if they do not communicate for more than ayear.

Referring briefly to FIG. 8, FIG. 8 illustrates electronic activitiesinvolving two nodes and the impact a time decaying relevancy score hason the connection strength between the two nodes. As shown in FIGS. 8,N1 and N2 may exchange a series of electronic activities. The nodepairing engine 240 or the system 200 can maintain a log of each of theelectronic activities involving both nodes. Each electronic activity canhave a unique electronic activity identifier and can identify a type ofactivity and maintain a time decaying relevancy score that can decreasein strength over time as time goes by. The node pairing engine 240 cancompute the connection strength in part by taking the sum of therespective time decaying relevancy score of each of the electronicactivities between the two nodes. In some embodiments, the node pairingengine 240 can take into account other factors for computing theconnection strength, for instance, by comparing one or more fields ofthe node profiles. For instance, nodes that belong to the sameorganization, report to each other via a clear reporting logic (and lackof reporting up alternative nodes) or have previously worked togethercan contribute to the connection strength between the nodes.

In certain embodiments, the node pairing engine 240 can determine that afirst node reports to a second node based on monitoring electronicactivity exchanged between the two nodes as well as electronic activitythat includes both nodes. In some embodiments, the node pairing engine240 can apply one or more rules to predict a relationship between twonodes based on the metadata information associated with the electronicactivities including both nodes.

In some embodiments, the connection strength between two nodes can begreater if the node pairing engine 240 can determine, from theelectronic activities involving the two nodes, a type of relationshipbetween the two nodes. For instance, if the node pairing engine 240 candetermine that one of the nodes is the only known superior node and theother of the nodes is the likely subordinate (instead of simply knowingthat the two nodes are colleagues or on the same team), the node pairingengine 240 can increase the connection strength between the two nodes.

In some embodiments, the node pairing engine 240 can be configured todetermine the connection strength between two nodes by monitoring thetype of electronic activities exchanged between them, the time of day,the day of the week, the mode of communication (email versus telephoneversus text message versus office phone versus cell phone), and theduration of such communications. The system 200 can determine that iftwo nodes are communicating over a weekend, the connection is strongerthan other connections that may only have communications limited toweekdays during office hours. The system 200 can also determine that theconnection strength between two nodes may be strong if the two nodes areresponding to each over the weekend, if they follow up with phone callsafter receiving emails, or other patterns that may indicated a strongconnection strength.

The node pairing engine 240 can be configured to identify a plurality ofnode pairs that have a strong connection strength. The node pairingengine 240 can then apply machine learning techniques to analyzeelectronic activities between the nodes of the node pair as well asanalyze the node profiles of each node and the nodes to which each ofthe nodes are connected. The node pairing engine 240 can then generate aconnection strength determination model that can be configured todetermine the connection strength between two nodes using the model thatis trained on node pairs known to have a strong connection strength. Insome embodiments, the node pairing engine can further train the modelwith node pairs that have a weak connection strength in a similarfashion.

The node parsing engine 240 or the tagging engine 265 can further tagthe connection between the nodes as professional, personal, colleagues,ex-colleagues, alumni, classmates, among others. These tags can beupdated as more and more electronic activities are processed over timeand the confidence score of these tags can be adjusted accordingly. Theconnection strength between nodes can be used by companies to determinewhich employee to assign to leads, accounts, or opportunities based onthe node's connections strengths with the lead, employees at theaccount, and employees of the account that may likely be working on theopportunity. Additional details about assigning employees to record suchrecord objects are described below with respect to Section 12.

N. Node Resolution

The node resolution engine 245 can be any script, file, program,application, set of instructions, or computer-executable code that isconfigured to enable a computing device on which the node resolutionengine 245 is executed to perform one or more functions of the noderesolution engine 245 described herein.

The node resolution engine 245 is configured to resolve nodes to whichelectronic activities are to be linked or otherwise associated. The noderesolution engine 245 can use the parsed information from the electronicactivity to identify values included in node profiles to determine amatch score between the electronic activity and a given node profile.The node resolution engine 245 can match the electronic activity to oneor more node profiles based on a match score between the electronicactivity and each of the node profiles exceeding a certain threshold.Different fields are assigned different weights based on the uniquenessof each value. In some embodiments, the uniqueness of each value can bedetermining how many node profiles include the same value for the givenfield relative to the total number of node profiles.

In some embodiments, the node resolution engine 245 may match theelectronic activity to the nodes between which the electronic activityoccurred. The node resolution engine 245 or the node pairing engine canestablish an edge between the two nodes corresponding to the electronicactivity.

In some embodiments, the node resolution engine 245 may not be able todetermine if the electronic activity matches any of the existing nodeprofiles maintained by the node profile manager. In some suchembodiments, the node resolution engine 245 can cause a new node profileto be generated and populated with values extracted from the electronicactivity. Before the node resolution engine 245 or other module of thesystem 200 determines to generate a new node, the node resolution engine245 can be configured to execute a node creation process. In someembodiments, the node resolution engine 245 can determine if themetadata of the electronic activity has attributes that are highconfidence that match, such as phone number, LinkedIn ID, or emailaddress. At the initial stage, the node resolution engine 245 can createa temporary node because not enough information is known to match theelectronic activity to an existing node. As a response to the electronicactivity is received, additional information can be parsed from theresponse to the electronic activity, which can then be used to furtherpopulate the temporary node. The temporary node can then be matched toexisting node profiles to determine if an existing node matches thetemporary node. If so, the temporary node can be merged with theexisting node profile. In some embodiments, the process of merginginvolves appending the temporary node with another node because theremight be mutually exclusive information that should be added.

In some embodiments, the node resolution engine 245 can perform identityresolution or deduplication based on one or more unique identifiersassociated with a node profile. For instance, if one system of recordprovides a first email address, uniquename@examplel.com and anothersystem of record provides a second email address, uniquename@example2.com, while there is not a direct match, the node resolution engine 245can resolve the two identifiers if there is a statistically significantnumber of matching or near matching fields, tags, or other statisticalresemblances.

In particular, the node resolution engine 245 can parse the stringbefore the @ in the email to determine one or more of a first name andlast name of the person. The node resolution engine 245 can applyseveral techniques to do so. First, the node resolution engine 245 cancheck to see if there are any rules in place for the domain name of theemail that indicate a particular pattern for assigning email addressesby the domain. For instance, does the company associated with the domainassign email addresses using any of the following conventions:firstname.lastname@domainname.com, FirstlnitialLastname@domainname.com,firstname@domainname.com, among others. This can be determined bylooking at node profiles (and email addresses) of other people belongingto the same company. Second, the node resolution engine 245 can parsethe string before the @ to attempt to recognize names from the strings.the node profile manager 220 maintains node profiles that include firstnames and last names and as such, the node resolution engine 245 canattempt to match a sequence of characters in the string to the list offirst names and last names to see if certain names are included in thestring. Upon identifying names from the string, the node resolutionengine 245 can determine if the name is typically a first name or a lastname based on a frequency of such names being first names or last names.Upon identifying the names with some level of statistical confidence,the node resolution engine 245 can identify a first name and a last nameof a person associated with the email address and may use the firstname, the last name and the company name to try and match the emailaddress to an existing node profile of the person.

In some embodiments, the node resolution engine 245 or the node profilemanager 220 can build a frequency distribution of first and last namesfrom information included in the node profiles maintained by the nodeprofile manager 220. The node resolution engine 245 can determine from afull name, a first name and a last name based on certain names beingmore common as last names and other names being more common as firstnames. The node resolution engine 245 can then determine a domain of theemail. The node resolution engine can then calculate the probabilitythat the string before the @ in the email corresponds to a person.

In some embodiments, the node resolution engine 245 can furtherdetermine if additional fields that could be matching—such as a socialhandle or a phone number to then have more surface to compare one nodeto other nodes to identify if any of the nodes can be merged.

In some embodiments, the node resolution engine can utilize time zonedetection to resolve if two nodes belong to the same person. The system200 can compute a time zone of each node by monitoring their electronicactivities and deducing that the time zone they are in is based on thetimes at which the electronic activities are ingested by the system 200.For instance, the node resolution engine 245 can determine that twonodes are different if the time zones deduced from their electronicactivity match different time zones.

In some embodiments, the node resolution engine 245 can be configured toperiodically perform deduplication by comparing each node to every othernode to determine if two nodes can be merged.

O. Systems of Record Data Extraction

The record data extractor 230 can be any script, file, program,application, set of instructions, or computer-executable code, that isconfigured to enable a computing device on which the record dataextractor 230 is executed to perform one or more functions of the recorddata extractor 230 described herein.

The record data extractor 230 can be configured to extract data from oneor more records of one or more systems of record. The record dataextractors 230 can identify record objects included in a system ofrecord and extract data from each of the record objects, includingvalues of particular fields. In some embodiments, the record dataextractor 230 can be configured to extract values of fields included inthe record object that are also included in the node profile maintainedby the node graph generation system 200.

P. Linking Electronic Activity to Systems of Record Data

The electronic activity linking engine 250 can be any script, file,program, application, set of instructions, or computer-executable code,that is configured to enable a computing device on which the electronicactivity linking engine 250 is executed to perform one or more functionsof the electronic activity linking engine 250 described herein.Additional details regarding the electronic activity linking engine isprovided below.

Q. Systems of Record Record Object Management

The record object manager 255 can be any script, file, program,application, set of instructions, or computer-executable code that isconfigured to enable a computing device on which the record objectmanager 255 is executed to perform one or more functions of the recordobject manager 255 described herein. The record object manager 255 canbe configured to maintain data regarding record objects of multiplesystems of record and can be configured to augment information for arecord object by extracting information from multiple record objectsacross a plurality of systems of record. The record object manager 255can function as a systems of record object aggregator that is configuredto aggregate data points from many systems of record, calculate thecontribution score of each data point, and a timeline of thecontribution score of each of those data points. The record objectmanager 255 or the system 200 in general can then enrich the node graphgenerated and maintained by the node graph generation system 200 byupdating node profiles using the data points and their correspondingcontribution scores. In certain embodiments, the record object manager255 can be further configured to utilize the data from the node graph toupdate or fill in missing data in a target system of record provided thedata in the node graph satisfies a predetermined confidence value.Additional details regarding the record object manager 255 is providedbelow.

R. Organizational Node Graph

The data source provider network generator 260 can be any script, file,program, application, set of instructions, or computer-executable code,that is configured to enable a computing device on which the data sourceprovider network generator 260 is executed to perform one or morefunctions of the data source provider network generator 260 describedherein. Additional details relating to the functionality of data sourceprovider network generator 260 are provided below with respect to thegeneration of a company cloud described in Section 9.

2. Systems and Methods for Linking Electronic Activity to Systems ofRecord

At least one aspect of the disclosure relates to systems and methods oflinking electronic activities to record objects of systems of record.The linking can be performed by the electronic activity linking engine250 (and other components) of the node graph generation system 200illustrated in FIG. 4.

Enterprises and other companies spend significant amount of resources tomaintain and update one or more systems of records. Examples of systemsof records can include customer relationship management (CRM) systems,enterprise resource planning (ERP) systems, document management systems,applicant tracking systems, among others. Typically, these systems ofrecords are manually updated, which can result in multiple issues.First, the information that is updated into the systems of records canbe incorrect either due to human error or in some cases, maliciousintent. Second, the information may not be updated in a timely manner.Third, employees may not be motivated enough to even update the systemsof records, resulting in systems of records that include outdated,incorrect, or incomplete information. To the extent that enterprisesrely on the data included in their systems of records to makeprojections or predictions, such projections and predictions may also beinaccurate as the data relied upon is also inaccurate. The presentdisclosure aims to address these challenges that enterprises face withtheir existing systems of records. In particular, the present disclosuredescribes systems and methods for linking electronic activities torecord objects included in one or more systems of record. Electronicactivities, such as electronic mail, phone calls, calendar events, amongothers, can be used to populate, update, and maintain states of recordobjects of systems of record. As electronic activities are exchangedbetween users, these electronic activities can be parsed to not onlyupdate a node graph as described above, but further update shadow recordobjects for one or more systems of records of enterprises that haveprovided access to such systems of record to the data processing system9300 shown in FIG. 3 or the node graph generation system 200. Asdescribed herein, the shadow record objects can be synced with therecord objects of the one or more systems of records of the enterprises.In some embodiments, the electronic activities can be used to directlyupdate the one or more systems of records of the enterprises withoutfirst updating a shadow record object. As described herein, and alsoreferring to FIG. 3, the updating of record objects with electronicactivity can refer to updating record objects within systems of record9360 and/or shadow record objects within the shadow systems of record.By way of the present disclosure, the node graph generation system 200can use the electronic activities to populate, maintain, and updatestates of record objects of systems of record.

As described herein, the node graph generation system 200 can includethe electronic activity linking engine 250 that is configured to linkelectronic activities to record objects of one or more systems ofrecord. By linking the electronic activities to such record objects, theelectronic activity linking engine 250 can be configured to updatestates of one or more record objects based on the electronic activities.

Linking electronic activities to record objects can also be referred toas matching or mapping the electronic activities to record objects.Linking the electronic activities to the record objects can providecontext to the electronic activities. The linked electronic activitiescan be stored in association with one or more record objects to whichthe electronic activity is linked in a system of record. Linking anelectronic activity to a record object can provide context to theelectronic activity by indicating what happened in the electronicactivity or record object, who was involved in the electronic activityor record object, and to what contact, node, person or business process,the electronic activity or record object should be assigned. Linking theelectronic activity to the record object can indirectly provide contextas to why the electronic activity occurred. For example, the linking ofelectronic activity, such as an email, to a lead record object (in thecontext or a customer relationship management system) can providecontext to the email that the email was sent to establish or further alead with the intent of converting the lead into an opportunity (and thelead record object into an opportunity record object). Although thedescription provided herein may refer to record objects and businessprocesses corresponding to customer relationship management systems, itshould be appreciated that the present disclosure is not intended to belimited to such systems of records but can apply to many types ofsystems of record including but not limited to enterprise resourceplanning systems, document management systems, applicant trackingsystems, among others. For the sake of clarity, it should be appreciatedthat electronic activities can be matched to record objects directlywithout having to link the electronic activities to node profiles. Insome embodiments, the electronic activities can be matched to nodeprofiles and those links can be used to match some of the electronicactivities to record objects.

Referring now to FIG. 9, FIG. 9 illustrates a block diagram of anexample electronic activity linking engine 250. The electronic activitylinking engine 250 can use metadata to identify a data source providerassociated with an ingested electronic activity and identify acorresponding system of record. The electronic activity linking engine250 can match the electronic activity to a record object of thecorresponding system of record. The electronic activity linking engine250 can include, or otherwise use, a tagging engine, such as the taggingengine 265 described above to determine and apply tags to the ingestedelectronic activities. The electronic activity linking engine 250 caninclude a feature extraction engine 310 to extract features from theelectronic activities that can be used to link electronic activitieswith one or more record objects of systems of records. In someembodiments, some of the features can include values corresponding tovalues stored in one or more node profiles maintained by the node graphgeneration system 200. The features, however, can include otherinformation that may be used to in conjunction with information alsoincluded in node profiles to link the electronic activity to one or morerecord objects included in one or more systems of record.

The electronic activity linking engine 250 can include a record objectidentification module 315 to identify which record object or objectswithin a system of record to match a given electronic activity. Theelectronic activity linking engine 250 can include a policy engine 320.The policy engine 320 can maintain policies that include strategies formatching the electronic activities to the record objects. The electronicactivity linking engine 250 can include a stage classification engine325 to determine a shadow stage for a given opportunity record object.The electronic activity linking engine 250 can include a linkrestriction engine 330 that can apply one or more policies from thepolicy engine 320 when linking electronic activities to record objects.The linking engine 250 can link the electronic activity to the recordobject identified by the record object identification module 315.Additional details regarding each of the components 310-335 are furtherprovided herein.

The features extraction engine 310 of the electronic activity linkingengine 250 can be any script, file, program, application, set ofinstructions, or computer-executable code, that is configured to enablea computing device on which the features extraction engine 310 isexecuted to extract or identify features from one or more electronicactivities and/or corresponding node profiles maintained by the nodegraph generation system 200 and use the extracted or identified featuresto generate corresponding feature vectors for the one or more electronicactivities.

The features extraction engine 310 can be a component of the electronicactivity parser 210 or otherwise interface with the electronic activityparser 210 to parse electronic activities and extract features fromelectronic activities. For example, the electronic activity parser 210can parse ingested electronic activities, such as, emails, calendarmeetings, and phone calls. The features extraction engine 310 can, foreach electronic activity, extract various features from the electronicactivity and in some embodiments, from one or more node profilescorresponding to the electronic activity, that the electronic activitylinking engine 250 can use to link the electronic activity to one ormore record objects of the one or more systems of record. In someembodiments, before an electronic activity can be linked to a recordobject of a system of record, the electronic activity can be matched toone or more node profiles in the node graph. In this way, the featuresextraction engine 310 can generate, based on the parsed data from theelectronic activity parser 210, a feature vector for the electronicactivity that can be used to link the electronic activity to a recordobject based on features extracted from the electronic activity as wellas one or more node profiles of the node graph.

The feature vector can be an array of feature values that is associatedwith the electronic activity. The feature vector can include each of thefeatures that were extracted or identified in the electronic activity bythe feature extraction engine 310. For example, the feature vector foran email can include the sending email address, the receiving emailaddress, and data parsed from the email signature. Each feature value inthe array can correspond to a feature or include a feature-value pair.For example, the contact feature “John Smith” can be stored in thefeature vector as “John Smith” or “name: John Smith” or “first name:John” “last name: Smith.” As described herein, the matching model 340can use the feature vector to match or link the electronic activity to arecord object. The feature vector can include information extracted froman electronic activity and also include information inferred from one ormore node profiles of the node graph generation system 200. The featurevector can be used to link an electronic activity to at least particularrecord object of a system of record by matching the feature values ofthe feature vector to a record object. For instance, if the featurevector includes the values “John” for first name and “Smith” for lastname, the electronic activity linking engine 250 can link the electronicactivity to a record object, such as a lead record object that includesthe name “John Smith” assuming other matching conditions are also met.

The features for an electronic activity can be explicit from theelectronic activity. The explicit features can be determined from themetadata or content of the electronic activity. For example, the“sender's email address” of an email can be parsed from the email'sheader value, as described in relation to FIG. 5A. In some embodiments,some features for an electronic activity can be derived from theelectronic activity. The derived features can be determined or impliedbased on explicit features of the electronic activity or determined fromnode profiles of the node graph described above. For example, an exampleelectronic activity may not include a name of the company to which thesender belongs. In such a case, the feature extraction engine 310 canextract the name of the company to which the sender belongs from a nodeprofile of the sender, which can include the name of the company. Thename of the company can be retrieved from the node profile of the senderand saved as a value in the feature vector once retrieved from the nodeprofile associated with the sender.

The features included in the feature vector for an electronic activitycan include features associated with the generator (or sender) of theelectronic activity and features associated with the recipient (orreceiver) of the electronic activity. For example, sender's emailaddress and the recipient's email address can both be used as featuresof the electronic activity. The features for an electronic activity caninclude, but are not limited to, a contact role, contact name, senderemail address, recipient email address, domain, list of recipient emailaddresses, estimated effort, and time, features extracted from emailcontents using natural language processing, features extracted fromemail signature, time of the email sent/delivery, among others. Thefeature vectors can be used to match electronic activities to recordobjects of one or more systems of record.

The feature extractor engine 310 can further identify one or more tagsassigned to an electronic activity or one or more node profilesassociated with the electronic activity by the tagging engine 265 andinclude those tags or information relating to those tags in the featurevector. In some embodiments, these tags can be used to provide contextto certain electronic activities, which can be used by the electronicactivity linking engine 250 to link electronic activities to recordobjects of one or more systems of records.

The record object identification module 315 can be any script, file,program, application, set of instructions, or computer-executable code,that is configured to enable a computing device on which the recordobject identification module 315 is executed to determine or select oneor more record objects to which an electronic activity should be linkedor matched.

Briefly referring to FIG. 10, among others, FIG. 10 illustrates aplurality of example record objects, and their interconnections. Therecord objects shown in FIG. 10 can be record objects or data records ofa system of record, such as a customer relationship management (CRM)system. It should be appreciated that other types of systems of recordsand record objects may exist and can be integrated with the node graphgeneration system 200. For instance, other systems of records caninclude Applicant Tracking Systems (ATS), such as Lever, located in SanFrancisco, Calif. or Talend by Talend Inc., located in Redwood City,Calif., enterprise resource planning (ERP) systems, customer successsystems, such as Gainsight located in Redwood City, Calif., DocumentManagement Systems, among others.

The systems of record can be one or more of shadow systems of record ofthe data processing system 9300 or the systems of record of the datasource providers. Additional details relating to the shadow systems ofrecord of the data processing system 9300 are provided below. Asillustrated in FIG. 10, the record objects can include a lead recordobject 1000, an account record object 1002, an opportunity record object1004, or a contact record object 1006. Each of the different types ofrecord objects can generally be referred to as record objects.

Each record object can be a data structure or data file into which datais stored or associated. The lead record object 1000 can be a lowquality object that includes unqualified contact information typicallyreceived through a web inquiry. A lead record object can correspond toone or more stages. Upon reaching a final “Converted” stage, a leadrecord object can be converted in a one-to-many relationship into aContact record object (person), an Account record object (company, ifnew, or added to existing account) and an Opportunity record object (ifthere is an opportunity for a deal here or added as contact role intoexisting opportunity).

For example, the lead record object 1000 can include the contactinformation for a lead or prospective buyer. The lead record object 1000can include fields, such as, Address, City, Company, CompanyDunsNumber,Description, Email, Industry, NumberOfEmployees, Phone, job title, andWebsite, among others.

The account record object 1002 can be a data structure that includesfields associated with an account that is held with the data sourceprovider. The fields can include AccountNumber, BillingAddress,Description, Industry, Fax, DunsNumber, LastActivityDate,MasterRecordld, Name, NumberOfEmployees, Ownership, Website,YearStarted, and IsPersonAccount, among others. A system of record caninclude an account record object 1002 for each of the data provider'scustomers. The system of record can include multiple account recordobjects 1002 for a given customer. For example, the system of record caninclude an account record object 1002 for each division of a givencustomer. The account record object 1002 can be stored with one or moreopportunity record objects 1004.

In some embodiments, the CRM can include partner record objects, whichcan also be referred to as partner account record objects. A partneraccount record object can be similar to an account record object. Thepartner account record object can include an additional field todesignate the record object as a partner account record object ratherthan a standard account record object. The partner account record objectcan be an account record object that is associated with a partner to thedata source provider. For example, the partner account record object canbe an account record object for a distributor of the data sourceprovider that distributes goods to the company of the account recordobject.

The opportunity record objects 1004 can be data structures that includea plurality of fields for a given opportunity. The opportunity canindicate a possible or planned deal with a customer for which an accountrecord object is already stored in the system of record. The opportunityrecord objects 1004 can include fields such as AccountId, Amount,CampaignId, CloseDate, Description, ExpectedRevenue, Fiscal,HasOpenActivity, IsClosed, IsWon, LastActivityDate, Name, OwnerId,StageName, Territory2Id, and Type, among others. One or more contactrecord objects 1006 can be associated with the account record object1002. The contact record objects 1006 can be data structures thatinclude fields associated with a contact. The contact record object 1006can include fields such as AccountId, AssistantName, Birthdate,Department, Description, DoNotCall, Email, Fax, FirstName,HasOptedOutOfEmail, HomePhone, LastName, MailingAddress, andMobilePhone, among others.

One or more contact record objects 1006 can be associated with anopportunity record object 1004 via an Opportunity Contact Role object(OCR). For example, a lead to sell a service to a potential customer canconvert into an opportunity record object 1004 when the customer beginsthe negotiation process to purchase the service. A contact record object1006 can be generated for each of the customer's employees involved inthe purchase. Each of the contact record objects 1006 can be associatedwith the opportunity record object 1004 for the sale via OpportunityContact Roles, which contain their own metadata about involvement ofspecific individuals in the opportunity, such as their Role in thisparticular opportunity or whether they are the Primary Contact of theAccount in this Opportunity.

In some embodiments, a lead record object 1000 can be converted into acontact record object 1006, an account record object 1002, and anopportunity record object 1004. For example, a lead record object 1000can be converted into a new contact record object 1006, account recordobject 1002, and opportunity record object 1004 once the lead recordobject 1000 after a predetermined number and nature of electronicactivities are associated with the lead record object 1000. Continuingthis example, the lead record object 1000 can be generated based on aweb inquiry from an interested party (lead) or via a cold email beingsent to a potential new customer. If the customer responds and passesqualification criteria, the lead record object 1000 can be convertedinto a new contact record object 1006, account record object 1002, andopportunity record object 1004. In some embodiments, the lead recordobject 1000 can be converted into a, for example, contact record object1006 that can get attached to or linked with an existing account recordobject 1002 and an existing opportunity record via an OpportunityContact Role record.

The fields of each of the different record object types can includehierarchical data or the fields can be linked together in a hierarchicalfashion. The hierarchical linking of the fields can be based on theexplicit or implicit linking of record objects. For example, a contactrecord object 1006 can include a “Reports To” field into which anidentifier of the contact can be stored. The “Reports To” field canindicate an explicit link in a hierarchy between two contact recordobjects 1006 (e.g., the first contact record object 1006 to the contactrecord object 1006 of the person identified by the “Reports To” field).In another example, the linking of the record objects can be implicitand learned by the electronic activity linking engine 250. For example,the electronic activity linking engine 250 can learn if multiplecustomers have the same value for a “Parent Account” field acrossmultiple system of record sources with high trust score and derive astatistically significant probability that a specific account belongs to(e.g., is beneath the record object in the given hierarchy) anotheraccount record object.

Referring to FIG. 9, among others, the record object identificationmodule 315 can determine, for a given electronic activity to whichrecord object the electronic activity should be linked. Linking theelectronic activity to one or more record objects can enable the status,metrics, and stage of the deal or opportunity to be tracked andanalyzed, or the context in which the electronic activity was performedto be understood programmatically. Linking electronic activities to therecord objects can also enable employee performance to be measured asdescribed herein. The record object identification module 315 canidentify a record object of one of the data processing system's shadowsystems of record using the feature vectors and node graph. In this way,the record object identification module 315 can assist, aid or allow theelectronic activity linking engine 250 to match the electronic activitywith a record object using one or more matching models 340.

The record object identification engine 315 can include one or morematching models 340. A matching model 340 can be trained or programmedto aid in matching electronic activities to record objects to allow theelectronic activity linking engine 250 to link the electronic activitiesto the matched record objects. For example, the record objectidentification engine 315 can include or use one or more matching models340 to assist, aid or allow the electronic activity linking engine 250to match electronic activities to record objects. In some embodiments,each of the one or more matching models 340 can be specific to aparticular data source provider, electronic activity type, or recordobject type. In some embodiments, the record object identificationengine 315 can include a single matching model that the record objectidentification engine 315 can use to match electronic activitiesingested by the data processing system 9300 to any number of a pluralityof record objects of a plurality of systems of records. In someembodiments, the matching models 340 can be data structures that includerules or heuristics for linking electronic activities with recordobjects. The matching models 340 can include matching rules (which canbe referred to as matching strategies) and can include restricting rules(which can be referred to as restricting strategies or pruningstrategies). As described further in relation to FIGS. 11 and 12, therecord object identification engine 315 can use the matching strategiesto select candidate record objects to which the electronic activitycould be linked and use the restricting strategies to refine, discard,or select from the candidate record objects. In some embodiments, thematching models 340 can include a data structure that includes thecoefficients for a machine learning model for use in linking electronicactivities with record objects.

In some embodiments, the matching model 340 used to link electronicactivities to one or more record objects can be trained using machinelearning or include a plurality of heuristics. For example, as describedabove the features extraction engine 310 can generate a feature vectorfor each electronic activity. The matching model 340 can use neuralnetworks, nearest neighbor classification, or other modeling approachesto classify the electronic activity based on the feature vector. In someembodiments, the record object identification engine 315 can use only asubset of an electronic activity's features to match the electronicactivity to a record object.

In some embodiments, the record object identification engine 315 can usematching models 340 trained with machine learning to match, for example,the electronic activity to a record object based on a similarity of thetext in and the sender of the electronic activity with the text in andsender of an electronic activity previously matched to a givenelectronic activity. In some embodiments, the matching model 340 can beupdated as electronic activities are matched to record objects. Forexample, a matching model 340 can include one or more rules to use whenmatching an electronic activity to a record object. If a user matches anelectronic activity to a record object other than the record object towhich the electronic activity linking engine 250 matched the electronicactivity, record object identification engine 315 can update thematching model 340 to alter or remove the rule that led to the incorrectmatching.

In some embodiments, once an electronic activity is matched with arecord object, a user can accept or reject the linking. Additionally,the user can change or remap the linking between the electronic activityand the record object. An indication of the acceptance, rejection, orremapping can be used to update the machine learning model or reorderthe matching strategies as discussed in relation to FIGS. 11 and 12. Theupdated model can be used in the future linking of electronic activityto nodes and the nodes to record objects by the record objectidentification engine 315. To train the machine learning models, thesystem can scan one or more systems of record that include manuallymatched electronic activity and record objects. The previous manuallymatched data can be used as a training set for the machine learningmodels.

In some embodiments, the matching model 340 can include a plurality ofheuristics with which the record object identification engine 315 canuse to link an electronic activity to one or more record objects. Theheuristics can include a plurality of matching algorithms that areencapsulated into matching strategies. The record object identificationengine 315 can apply one or more matching strategies from the matchingmodels 340 to the electronic activity to select which record object (orrecord objects) to link with the electronic activity. In someembodiments, the record object identification engine 315 can use thematching strategies to select candidate record objects to which theelectronic activity can be linked. The record object identificationengine 315 can use a second set of strategies (e.g., restrictingstrategies) to prune the candidate record objects and select to which ofthe candidate record objects the electronic activity should be linked.

The application of each strategy to an electronic activity can result inthe selection of one or more record objects (e.g., candidate recordobjects). The selection of which matching strategies to apply to anelectronic activity can be performed by the policy engine 320. Thepolicy engine 320 is described further below, but briefly, the policyengine 320 can generate, manage or provide a matching policy for each ofthe data source providers 9350. The policy engine 320 can generate thematching policy automatically. The policy engine 320 can generate thematching policy with input or feedback from the data source provider9350 to which the matching policy is associated. For example, the datasource provider (for example, an administrator at the data sourceprovider) can provide feedback when an electronic activity isincorrectly linked and the matching policy can be updated based on thefeedback.

A given matching policy can include a plurality of matching strategiesand the order in which the matching strategies should be applied toidentify one or more record objects to which to link the electronicactivity. The record object identification module 315 can apply one ormore of the plurality of matching strategies from the matching models340, in a predetermined order specified or determined via the matchingpolicy, to identify one or more candidate record objects. The recordobject identification module 315 can also determine, for each matchingstrategy used to identify a candidate record object, a respective weightthat the record object identification module 315 should use to determinewhether or not the candidate record object is a good match to theelectronic activity. The record object identification module 315 can beconfigured to compute a matching score for each candidate record objectbased on the plurality of respective weights corresponding to thematching strategies that were used to identify the candidate recordobject. The matching score can indicate how closely a record objectmatches the electronic activity based on the one or more matchingstrategies used by the record object identification module 315.

One or more of the matching strategies can be used to identify one ormore candidate record objects to which the electronic activity linkingengine can match a given electronic activity based on one or morefeatures (e.g., an email address) extracted from the electronic activityor tags assigned to the electronic activity. In some embodiments, thefeatures can be tags assigned by the tagging engine 265. In someembodiments, the electronic activity can be matched to a node profilethat is already matched to a record object, thereby allowing the recordobject identification module 315 to match the electronic activity to arecord object previously matched or linked to a node profile with whichthe electronic activity may be linked. In addition, the matchingstrategies can be designed or created to identify candidate recordobjects using other types of data included in the node graph generationsystem, or one or more systems of record, among others. In someembodiments, the matching strategies can be generated by analyzing howone or more electronic activities are matched to one or more recordobjects, including using machine learning techniques to generatematching strategies in a supervised or unsupervised learningenvironments.

Subsequent strategies can be applied to prune or restrict the recordobjects that are selected as potential matches (e.g., candidate recordobjects). For example, and also referring to FIG. 11, FIG. 11illustrates the restriction of a first grouping 1102 of record objectswith a second grouping 1106 of record objects. A first plurality ofstrategies 1100 can be applied to select a first grouping 1102 of recordobjects. A second plurality of strategies 1104 can be applied toidentify a second grouping 1106 of record objects that can be used torestrict or prune the first grouping 1102 of record objects. Forexample, the record object identification module 315 can select therecord object to which the electronic activity is linked from theoverlap 1108 of the groupings 1102 and 1106.

For example, and also referring to FIG. 12, among others, FIG. 12illustrates the application of a first plurality of matching strategiesand a second plurality of matching strategies to generate one or moregrouping of record objects and then selecting record objects thatsatisfy both the first plurality of matching strategies and the secondplurality of matching strategies. In some embodiments, the firstplurality of matching strategies can be configured to generate the firstgrouping 1102 of record objects shown in FIG. 11, while the secondplurality of matching strategies 1104 can be configured to generate thesecond grouping 1104 of record objects. In some embodiments, the firstplurality of matching strategies 1100 can be associated with one or morerecipients of the electronic activity to be matched and the secondplurality of matching strategies 1104 can be associated with a sender ofthe electronic activity to be matched. The candidate record objectsselected by the first plurality of matching strategies 1100 and thesecond plurality of matching strategies 1104 can be filtered, pruned orotherwise discarded from being matched with the electronic activityusing restricting strategies (described further below). In someembodiments, the first plurality of strategies can be referred to asbuyer-side or recipient-side strategies and the second plurality ofstrategies can be referred to as seller-side or sender-side strategies.The policy engine 320 can select one or more matching strategies of thefirst plurality of matching strategies 1100, second plurality ofmatching strategies 1104 and restricting strategies for the recordobject identification engine 315 to apply in a predetermined order. Thematching strategies of the first plurality of matching strategies 1100and the second plurality of matching strategies 1104 can each beconfigured to select one of the types of record objects. For example,the matching strategies 1100 and 1104 can each be configured to selectone of a lead record object 1000, an account record object 1002, anopportunity record object 1004, a partner record object, among others.For example, a matching strategy can be used to match an electronicactivity to an account record object 1002 in the shadow system ofrecords based on an email address extracted from the electronic activityvia a number of sequentially used matching strategies. The restrictionstrategies can be used to remove one or more record objects that areselected by any of the first plurality of matching strategies 1100 orany of the second plurality of matching strategies 1104.

In an example where the electronic activity includes the email“john.smith@example.com,” the record object identification module 315can use a first matching strategy, such as a matching strategy forselecting the account record object based on email addresses to identifyone or more candidate record objects that may match the email addressfield of the electronic activity. First, the record objectidentification module 315 can return all contact record objects with“john.smith@example.com” in the email field. The record objectidentification engine 315 can then identify the account record objectsthat are linked with each of the contact record objects with“john.smith@example.com” in the email field.

In some embodiments, if the system was not able to find a contact recordobject with the field (or other fields) containing“john.smith@example.com”, the system can use a secondary matchingstrategy 1100 and find an account record object with the domain namethat matches the domain name of the email “@example.com”. If afterapplying the restricting strategies and eliminating possible options,only one account with such domain name is left, the system would haveidentified the account to which potential contact with email address“john.smith@example.com” should belong and the original electronicactivity should be linked to. In this case, the system couldautomatically create a contact record with email“john.smith@example.com”, linked to the account record with domain name“example.com” and then associate electronic activity from which thisprocess started to the newly created contact record object and rightaccount record object. It is worth noting that the order in whichmatching strategies 1100 and 1104 and the restriction strategies areapplied can impact and modify outcomes of matching model 340.

Still referring to FIG. 12, the record object identification engine 315can use one or more of the matching strategies 1100 associated withaccount record objects to generate a matched candidate record objectarray 1202 that identifies one or more candidate record objects thatwere identified based on the matching strategies 1100 associated withaccount record objects. The record object identification engine 315 cangenerate three matched record object arrays 1202. Each of the matchedrecord object arrays can be associated with a different one of therecord object types. For example, the record object identificationengine 315 can generate an account record object array, an opportunityobject array, a contact object array, a lead object array, and a partnerobject array (not shown). The results (e.g., the returned recordobjects) for a given matching strategy 1100 can be appended to therecord object array 1202 for the associated record object type. Forexample, matching strategy 1100(1) can be used to return the accountrecord objects with UIDs A1 and A17, the matching strategy 1100(2) canbe used to return the account record object with the UID A93, and thematching strategy 1100(3) can be used to return the account recordobject with the UIDs A123 and A320.

The recipient-side matching strategies 1100 can include a plurality ofmatching strategies. The matching strategies can be arranged in apredetermined and configurable order. The matching strategies of therecipient-side strategies 1100 can include one or more of matching toopportunity record objects based on contact role, matching to accountrecord objects based on contact record objects, matching to accountrecord objects based on domains, matching to opportunity record objectsbased on contacts, matching to partner account record objects based oncontacts, matching to partner account record objects using domains,among others. The record object identification engine 315 can use therecipient-side strategies 1100 to select a plurality of candidate recordobjects to form record object arrays 1202.

Each value in the matched record object arrays 1202 can include anindication of one of the record objects that was matched using thematching strategies (e.g., the recipient-side strategies 1100). Forexample, the matched record object arrays 1202 can include an array ofUIDs associated with each of the record objects that were matched by therecord object identification engine 315 using the matching strategies.In some embodiments, each value in the array can be a data pair thatincludes the matched record object UID and a score indicating howconfident the system is on the match between the electronic activity andthe record object. The score can be based on the matching strategy whichreturned the given record object. In some embodiments, the score may beadjusted based on previous matches and how a user accepted or modifiedthe previous matches. In some embodiments, a record object can beselected multiple times; for example, a first and a second matchingstrategy can each select a given record object. A score can beassociated with each matching strategy and the score for the recordobject selected by multiple matching strategies can be an aggregate (forexample, a weighted aggregate) of the scores associated with each of thematching strategies that selected the record object. The scores canindicate how well the selected record object satisfied the one or morematching strategies.

The record object identification engine 315 can select record objectsbased on matching strategies for each of the participants associatedwith the electronic activity. For example, the electronic activity canbe an email with a sender and a plurality of recipients. The sender andthe plurality of recipients can be the participants that are associatedwith the electronic activity. The record object identification engine315 can apply each of the matching strategies for each of theparticipants. Multiple matching strategies for a given participant canreturn the same record object multiple times. A matching strategyapplied to multiple participants can return the same record objectmultiple times. The score that the record object identification engine315 assigns to each selected record object can be based on the number oftimes the given record object was returned after the matching strategieswere applied for each of the electronic activity's participants. Forexample, a first record object can be returned or selected four timesand a second record object can be returned or selected once. The recordobject identification engine 315 can assign the first record object ahigher relative score than the second record object that was onlyselected once.

In some embodiments, the record object identification engine 315 canselect record objects using matching strategies that select recordobjects based on tags. The electronic activity can be parsed with anatural language processor and the tags can be based on terms identifiedin the electronic activity. Parsing the electronic activity with thenatural language processor can enable the electronic activity to bematched to record objects by mention. For example, the electronicactivity can be parsed and the term “renewal” can be identified in theelectronic activity. A “renewal” tag can be applied to the electronicactivity. A matching strategy to select record objects based on tags canselect a renewal record object opportunity with the electronic activityand include the renewal record object opportunity in the record objectarray 1202. In another example, the system 200 can identifyidentification numbers contained in the electronic activity for whichtags can be assigned to the electronic activity. The identificationnumbers can include serial numbers, account numbers, product numbers,etc. In this example, and assuming a tag identifying an account numberis assigned to the electronic activity, a matching strategy to selectrecord object based on tags can select an account record object thatincludes a field with the account number identified in the electronicactivity's tag.

The record object identification engine 315 can apply one or more of aplurality of sender-side strategies 1104 that can be used to select oneor more candidate record objects included in one or more second set ofrecord object arrays 1204. In some embodiments, the record objectidentification engine 315 can apply one or more of a plurality ofsender-side strategies 1104 to restrict or prune the record objectsselected using the matching strategies 1100. By applying the set ofsender-side strategies 1104, the record object identification engine 315can generate the second set of record object arrays 1204 that can beused to prune or restrict the first set of record object arrays 1202.For example, the record object identification engine 315, applying asender-side strategy 1104 that selects accounts record objects based onan account owner, can select the account record object with UID A17 andA123. The record object identification engine 315 can use sender-sidestrategies such as selecting record objects for matching based onaccount teams associated with one or more participants of the electronicactivity. For example, the record object identification engine 315 canselect a record object that identifies the sender of the electronicactivity. as a member of the account team associated with the recordobject.

The record object identification engine 315 can prune the identifiedcandidate record object by determining the intersection of the first setof record object arrays 1202 (produced with matching strategies 1100)and the second set of record object arrays 1204 (produced with matchingstrategies 1104). For example, the account record object array 1202generated with the set of matching strategies 1100 is, in the exampleillustrated in FIG. 12, {A1, A17, A93, A123, A320}. The account objectarray 1204 generated with the set of sender-side strategies 1104 is{A17, A123}. The record object identification engine 315 can determinethat the intersection array 1206 of the account record object array 1202and account record object array 1204 is {A17, A123}. In this way, thesender-side strategy restricted the record objects A1, A93 and A320 frombeing selected as a match to the incoming electronic activity. Therecord object identification engine 315 can combine the intersectionarrays 1206 generated by the intersection of the sender-side strategies1104 and the recipient-side strategies 1100 to generate an output array1208. The output array 1208 can include indications of record objectsand the weights or scores associated with each of the record objects.

The record object identification engine 315 can also use restrictionstrategies to further prune or restrict out record objects selectedusing the matching strategies 1100 and 1104. The record objectidentification engine 315 can use the restriction strategies to selectone or more record objects to which the electronic activity should notbe matched. For example, although this example is not reflected in FIG.12, the record object identification engine 315 can use a restrictionstrategy to select record objects A1 and A17 to generate a restrictionrecord object array including {A1, A17}. If, using the recipient-sidematching strategies, the record object identification engine 315 selectsrecord objects A1, A3, A10, and A17 to generate {A1, A3, A10, A17}, therecord object identification engine 315 can remove A1 and A17 from therecord object array because they were identified in the restrictionrecord object array as record object to which the electronic activityshould not be matched.

In some embodiments, the record object identification engine 315 canapply the restriction strategies once the record object identificationengine 315 selects one or more record objects with the sender-sidestrategies 1104 or the recipient-side strategies 1100. The record objectidentification engine 315 can apply the restriction strategies beforethe record object identification engine 315 selects one or more recordobjects with the sender-side and recipient-side strategies. For example,the restriction strategies can be one of the below-described matchingfilters.

In some embodiments, the output array 1208 can include one or morerecord objects that can be possible matches for the electronic activity.The selection from the output array 1208 can be performed by the belowdescribed record object identification engine 315. If the output array1208 only includes one record object, the electronic activity can bematched with the record object of the output array 1208. In someembodiments, the electronic activity is only matched with the recordobject if the confidence score of the record object is above apredetermined threshold. The confidence score of the record objectindicates a level of confidence that the record object is the correctrecord object to which to link the electronic activity. If the outputarray 1208 includes multiple record objects, the electronic activity canbe matched with the record object having the highest confidence score(given that the highest confidence score is above the predeterminedthreshold). If the output array 1208 does not include any recordobjects, the confidence score of the record objects are not above thepredetermined threshold, or multiple record objects have the sameconfidence score above the predetermined threshold, the system canrequest input from the user as to which record object to match theelectronic activity. In these cases, the matching strategies can beupdated based on the input from the user.

In some embodiments, the record object identification engine 315 cangroup or link contact record objects on one or both sides of a businessprocess into groups. The record object identification engine 315 can usethe groups in the matching strategies. For example, the record objectidentification engine 315 can group users on a seller side into accountteams and opportunity teams. Account teams can indicate a collection ofusers on the seller side that collaborate to close an initial oradditional deals from a given account. Opportunity teams can be acollection of users on the seller side that collaborate to close a givendeal. The record object identification engine 315 can add a user to anaccount or opportunity team by linking the contact record object of theuser to the given account team record object or opportunity team recordobject. The record object identification engine 315 can use accountteam-based matching strategies or opportunity team-based matchingstrategies to select record objects with which the electronic activitycan be matched.

In some embodiments, at periodic intervals, the record objectidentification engine 315 can process the electronic activities linkedwith account record objects and opportunity record objects to generateaccount teams and opportunity teams, respectively. For a given accountrecord object, the record object identification engine 315 can count thenumber of times that a seller side user interacts with the accountrecord object (for example, is included in an electronic activity thatis linked or matched to the account record object). For example, therecord object identification engine 315 can count the number of timesthe user was included on an email or sent an email that was linked withthe account record object. If the count of the interactions is above apredetermined threshold, the record object identification engine 315 canadd the user to an account team for the account record object. In someembodiments, the count can be made over a predetermined time frame, suchas within the last week, month, or quarter. The record objectidentification engine 315 can perform a similar process for generatingopportunity teams. In some embodiments, the account teams andopportunity teams can be included in the matching and restrictionstrategies used to match an electronic activity with a record object.Conversely, if the count of the interactions of a particular user isbelow a predetermined threshold within a predetermined time frame (forexample, a week, a month, three months, among others), the record objectidentification engine 315 can remove the user from the account team orthe opportunity team.

In some embodiments, the record object identification engine 315 canselect record objects with which to match a first electronic activitybased on a second electronic activity. The second electronic activitycan be an electronic activity that is already linked to a record object.The second electronic activity can be associated with the firstelectronic activity. For example, the system 200 can determine that thefirst and second electronic activities are both emails in a threadedemail chain. The system can determine the emails are in the same threadusing a thread detection policy. The thread detection policy can includeone or more rules for detecting a thread by comparing subject lines andparticipants of a first email and a second email or in some embodiments,by parsing the contents of the body of the second email to determine ifthe body of the second email includes content that matches the firstemail and email header information of the first email is included in thebody of the second email. If the second electronic activity is anearlier electronic activity that is already matched to a given recordobject, the record object identification engine 315 can match the firstelectronic activity to the same record object.

The policy engine 320 can be any script, file, program, application, setof instructions, or computer-executable code that is configured toenable a computing device on which the policy engine 320 is executed tomanage, store, and select matching strategies. The policy engine 320 cangenerate, manage, and store one or more matching strategy policies foreach of the data source providers. For example, the policy engine 320can generate matching strategy and restriction strategy policies foreach division or group of users within a data source provider.

In some embodiments, a matching policy can include a data structure thatindicates which matching strategies to apply to an electronic activityfor a given data source provider. For example, the matching policy caninclude a list of matching strategies that are used to select recordobjects. The list of matching strategies can be manually created by auser or automatically generated or suggested by the system. In someembodiments, the policy engine can learn one or more matching strategiesbased on observing how one or more users previously matched electronicactivities to record objects. These matching strategies can be specificto a particular user, group, account, company, or across multiplecompanies. In some embodiments, the policy engine can detect a change inlinkages between one or more electronic activities and record objects inthe system of record (for example, responsive to a user linking anelectronic activity to another object inside a system of recordmanually). The policy engine can, in response to detecting the change,learn from the detected change and update the matching strategy orcreate a new matching strategy within the matching policy. The policyengine can be configured to then propagate the learning from thatdetected change across multiple matching strategies corresponding to oneor more users, groups, accounts, and companies. The system can also beconfigured to find all past matching decisions that would have changedhad the system detected the user-driven matching change before, andupdate those matching decisions retroactively using the new learning.

In some embodiments, the matching policy can also identify whichrestriction strategies to apply to an electronic activity for a givendata source provider. For example, the restriction policy can include alist of restriction strategies that are used to restrict record objects.The list of restriction strategies can be manually created by a user orautomatically generated or suggested by the system. In some embodiments,the policy engine can learn one or more restriction strategies based onobserving how one or more users previously matched or unmatchedelectronic activities to record objects. These restriction strategiescan be specific to a particular user, group, account, company, or acrossmultiple companies. In some embodiments, the policy engine can detect achange in linkages between one or more electronic activities and recordobjects in the system of record (for example, responsive to a userlinking or unlinking an electronic activity to another object inside asystem of record manually). The policy engine can, in response todetecting the change, learn from the detected change and update therestriction strategy or create a new restriction strategy within therestriction policy. The policy engine can be configured to thenpropagate the learning from that detected change across multiplerestriction strategies corresponding to one or more users, groups,accounts, and companies. The system can also be configured to find allpast matching decisions that would have changed had the system detectedthe user-driven restriction change before, and update those matchingdecisions retroactively using the new learning.

The policy engine 320 can update the matching policy with input orfeedback from the data source provider to which the matching policy isassociated. For example, the data source provider can provide feedbackwhen an electronic activity is incorrectly linked and the matchingpolicy can be updated based on the feedback. Updating a matching policycan include reordering the matching strategies, adding matching orrestriction strategies, adjusting individual matching strategy behavior,removing matching strategies, or adding restriction strategies. The linkrestriction engine 330 can be any script, file, program, application,set of instructions, or computer-executable code, that is configured toenable a computing device on which the link restriction engine 330 isexecuted to limit to which record objects an electronic activity can belinked.

In some embodiments, data source providers can generate restrictionpolicies or restriction strategies that include rules that indicateconditions under which electronic activities should not be linked torecord objects. For example, restriction policies can include rules thatprevent internal emails from being linked to a record object. Otherrestriction policies can limit bot emails (e.g., emails sent to aplurality of people or an email sent as an automatic reply), non-personelectronic activity (e.g., electronic activity, such as calendaractivity, associated with an asset, such as a conference room),activities, related to persons, who are working in sensitive orunrelated positions (e.g. HR employees), activities, related to personswho do not “own” specific records in the system of record or who do notbelong to specific groups of users, or to private or personal electronicactivities (e.g., non-work-related emails). These restriction policiesor restriction strategies can include one or more matching filtersdescribed herein.

The restriction policies can be generated automatically by the system orcan be provided by the data source provider. Different restrictionpolicies can be linked together to form a hierarchy of restrictionpolicies, preserving the order in which they should be applied. Forexample, restriction policies can be set and applied at a group nodelevel (e.g., company level), member node level (e.g., user level),account level, opportunity level, or team level (e.g., groups of userssuch as account teams or opportunity teams). For example, a restrictionpolicy applied at the company level can apply to the electronic activitysent or received by each employee of the company while a restrictionpolicy applied at the user level is only applied to the electronicactivity sent or received by the user.

The link restriction engine 330 can use the restriction policies toremove or discard record objects from the output array 1208. Forexample, if a restriction policy indicates that electronic activity froma given employee should not be linked to record object A17 and recordobject A17 is included in the output array 1208, the link restrictionengine 330 can remove record object A17 from the output array 1208.

In some embodiments, the link restriction engine 330 can apply therestriction policies to electronic activities prior to the matchingperformed by the record object identification module 315. For example,if a restriction policy includes rules that calendar-based electronicactivity for a conference room should not be linked to any recordobject, the link restriction engine 330 can discard or otherwise preventthe record object identification module 315 from linking the electronicactivity to a record object.

The tagging engine 265 can be any script, file, program, application,set of instructions, or computer-executable code that is configured toenable a computing device on which the tagging engine 265 is executed togenerate tags for the electronic activity. The tagging engine 265 cangenerate or add tags to electronic activity based on informationgenerated or otherwise made available by the record objectidentification module 315 and the matching model 340. The tagging engine265 can generate a tag array that includes each of the plurality of tagsassigned or associated with a given electronic activity. By having tagsassigned to electronic activities the node graph generation system 200can be configured to better utilize the electronic activities to moreaccurately identify nodes and record objects to which the electronicactivity should be linked.

In addition to the above described tags, the tagging engine 265 canassign tags to an electronic activity based on the output of the recordobject identification module 315 and matching model 340, among othercomponents of the system described herein. For example, the taggingengine 265 can add one or more tags indicating to which record objectsthe record object identification module 315 returned as candidate recordobjects for the electronic activity. For example, and also referring toFIG. 12, the tagging engine 265 can add tags to indicate each recordobject contained within the output array 1208. In some embodiments, thetagging engine 265 can add a tag for each record object contained withinthe output array 1208. In some embodiments, the tagging engine 265 canadd a tag for each record object contained within the output array 1208.In some embodiments, the tagging engine 265 can include a tag only forthe record object in the output array 1208 that most closely matches theelectronic activity.

The linking generator 335 can be any script, file, program, application,set of instructions, or computer-executable code that is configured toenable a computing device on which the linking generator 335 is executedto link electronic activities to record objects. As described above, thesystem can generate and maintain a shadow system of record for each of adata source provider's system of record. The data source provider'ssystem of record can be referred to as a master system of record ortenant-specific system of record. The linking generator 335 can select arecord object from the record object array 1208 and link the electronicactivity to the selected record object in the shadow system of record.For example, the record object identification engine 315 can use theconfidence scores of the record objects in the record object array toselect a record object with which to match the electronic activity.

Also referring to FIG. 12, the linking generator 335 can link theelectronic activity to one or more of the record objects included in theoutput array 1208. In some embodiments, the linking generator 335 canlink the electronic activity to one or more record objects in the outputarray 1208. For example, the linking generator 335 may only link theelectronic activity to the record object in the output array 1208 thatmost closely matches the electronic activity. In some embodiments, thelinking generator 335 links the electronic activity with only one of therecord objects in the output array 1208 (e.g., the record object havingthe highest score).

Linking the electronic activity with a record object can include savingthe electronic activity (or an identifier thereof) into the shadowsystem of record in association with the record object. For example,each record object can include a unique identifier. The electronicactivity can be saved into the system of record and the record object'sunique identifier can be added to a record object field of theelectronic activity to indicate to which record object the electronicactivity is linked. In some embodiments, each electronic activity can beassigned a unique identifier. The electronic activity's uniqueidentifier can be added to a field in the shadow record object toindicate that the electronic activity is associated with the shadowrecord object. In some embodiments, the shadow record object can bematched or synced with a record object in a client's system. When theshadow record object and the record object are synced, data, such as theelectronic activity's unique identifier in the above example, can becopied to the corresponding field in the matched record object of theclient's system. For example, if the linking generator 335 matches anemail to a given record object in the shadow system of record, whensynced the email can be matched to the given record object in theclient's system of record.

By linking the electronic activities to record objects, the system cangenerate metrics regarding the electronic activities. The metrics caninclude engagement scores for users, employees, specific deals oropportunities, managers, companies, or other parties associated with asystem of record. Additional details regarding metrics and thecalculation thereof are described below in Section 11, among others. Theengagement scores can indicate amongst other things how likely anopportunity (or deal) is to close successfully (or unsuccessfully) orwhether the number of contacts in the account are sufficiently engagedwith the sales representative to prevent the account from disengagingwith the company. The engagement scores can provide an indication of anemployee's productivity and can indicate whether the user should receiveadditional training or can indicate whether the user is on track toachieve predefined goals. The metrics can be calculated dynamically asthe electronic activities are matched to nodes and record objects or themetrics can be calculated in batches, at predetermined intervals.Metrics can also be based on the content or other components of theelectronic activity in addition to or in place of the linking of theelectronic activity to a node and record object.

For example, FIG. 13 illustrates an example calculation for calculatingthe engagement score of an opportunity record object. The examplecalculation can include an electronic activity weight 1300, a volumevector 1302 indicating a count of each electronic activity type, aseniority weight 1304, and a department weight 1306. As illustrated inFIG. 13, the electronic activity linking engine 250 can determine theengagement score by collecting each of the electronic activitiesassociated with a given opportunity record object. The electronicactivity linking engine 250 can count the volume (e.g., number) of eachtype of electronic activity linked with the opportunity record object.For example, the electronic activity linking engine 250 can tag eachingested electronic activity as being an in-person meeting electronicactivity, a conference call electronic activity, a received emailelectronic activity, a sent email electronic activity, a cold emailelectronic activity, a blast email electronic activity, or a call, amongothers. The electronic activity linking engine 250 can also tag theelectronic activity using NLP. For example, electronic activity linkingengine 250 can tag an email based on mentions of a competitor, product,specific people, specific places, or other phrases contained within theelectronic activity. The electronic activity linking engine 250 can alsogenerate tags based on the combination of other tags, linkinginformation, and fields within linked objects.

The count of each of the different types of electronic activities can bestored in the volume vector 1302. The volume vector 1302 can bemultiplied by the weight or points assigned to each of the differentelectronic activities. The weight or points associated with each of theelectronic activity types in the electronic activity weight can indicatethe significance of the electronic activity to the successful completionof the deal. In some embodiments, the weights can be set by theelectronic activity linking engine 250. The weights can be set based onthe sales motion of the given tenant or data source provider. Eachweight can be a normalized value that can represent the significance agiven feature, or collection of electronic activities. For example, anemail including the VP of Sales can be given a higher weight whencompared to an email that only includes managers. In some embodiments,the electronic activity linking engine 250 can reference anorganizational hierarchy derived from the node graph and assignrelatively higher weights to electronic activities that involve peoplerelatively higher in the organizational hierarchy. For example, havingrepeated, in-person meetings with a CxO at a prospective client orcompany can be more beneficial to the successful closing of the dealthan cold calling a random contact at the company. Accordingly, thein-person meeting is assigned a higher weight (50 points) that the call,which is assigned a relatively lower weight of 1.

The engagement score can also be based on a seniority weighting factor.The seniority weighting factor can then be applied to the volumeweighted scores of the electronic activities. The seniority weightingfactor can apply a weighting based on those included on or involved withthe electronic activity. In some embodiments, the feature extractionengine 310 can determine which contacts or people are associated withelectronic activity. For example, the feature extraction engine 310 canparse the TO: and CC: fields of an email (an example electronicactivity) and then, using the node graph, determine seniority,department, job title, or role of each contact listed on the email attheir current and past roles. In some embodiments, the seniorityweighting factor can be based on the contact record objects to which thematching model 340 (or other component of the system) matched theelectronic activity.

The engagement score can also be based on a department weighting factor.The department weighting factor can be normalized across all thedepartments (such as within a company or account). In some embodiments,once the system determines which contacts are associated with theelectronic activity, as described above, the system can determine thedepartment of each of the contacts using the node graph.

The stage classification engine 325 can be any script, file, program,application, set of instructions, or computer-executable code, that isconfigured to enable a computing device on which the stageclassification engine 325 is executed to determine or predict a stage ofa deal or opportunity.

In some embodiments, record objects can be associated with a pluralityof stages. In some embodiments, the record object can be an opportunityrecord object or any other record object that describes a businessprocess, such as a sales process, a hiring process, or a support ticket.The stages can be defined by the system or by the data source provider.

Using the example of an opportunity record object in a sales process,the stages can indicate the steps taken in an opportunity or deal fromthe beginning of the deal to the final disposition of the deal (e.g.,close and won or closed and lost). The stages can include, but are notlimited to: prospecting, developing, negotiation, review, closed/won, orclosed/lost.

Each of the stages can be linked to different tasks or milestones. Forexample, a sales representative can develop a proposal during the“developing” stage. Each of the stages can be linked to differentactions taken by the sales representative or prospect contacts,associated contacts or other people. For example, initially during theprospecting and developing stages a sales representative may be involvedin the opportunity or deal. At a later stage, such as negotiations, asales manager may become involved in the deal.

The stages can be based on the contacts present or involved on bothsides of the deal. For example, as the deal advances to higher stages,more senior people may be included in the electronic activities. Thestage of the deal can be based on the identification or introduction ofan opportunity contact role (OCR) champion. In some embodiments, anadministrator or user of the system of record can link the opportunityrecord object with a contact record object and designate the contact ofthe contact record object as an opportunity contact role. The championcan be a person on the buyer side of the deal that will support andprovide guidance about the deal or opportunity to the seller side. Insome embodiments, the OCR champion can be selected based on one or morerules. For example, the one or more rules can include setting the personidentified as the VP of sales (or other specific role) as the OCRchampion. In some embodiments, the OCR champion can be selected based onhistorical data. For example, the historical data can indicate that in90% of the past deals a specific person or role was the OCR champion.Based on the historical data, when the person is added as a recipient ofan electronic activity, the person can be identified as the OCRchampion. The OCR champion can also be identified probabilisticallybased on tags associated with the electronic activities linked to theopportunity record object or content within the electronic activities.

In some embodiments, OCRs can be configurable by the company on anaccount by account basis. Depending on the type, size or nature of theopportunity, the customer or account involved in the opportunity mayhave different types and numbers of OCRs involved in the opportunityrelative to other opportunities the same customer is involved in.Examples of OCRs can include “Champion,” “Legal,” “Decision Maker,”“Executive sponsor” among others.

The system 200 can be configured to assign respective opportunitycontact roles to one or more contacts involved in an opportunity. Thesystem 200 can be configured to determine the opportunity contact roleof a contact involved in the opportunity based on the contact'sinvolvement. In some embodiments, system 200 can determine the contact'srole based on a function the contact is serving. The function can bedetermined based on the contact's title, the context of electronicactivities the contact is involved in, and other signals that can bederived from the electronic activities and node graph. In addition, thesystem 200 can assign the contact a specific opportunity contact rolebased on analyzing past deals or opportunities in which the contact hasbeen involved and determining which opportunity contact role the contacthas been assigned in the past. Based on historical role assignments, thesystem 200 can predict which role the contact should be assigned for thepresent opportunity. In this way, the system 200 can makerecommendations to the owner of the opportunity record object to addcontacts to the opportunity or assign the contact an opportunity contactrole.

In some embodiments, the system 200 can determine that a contact shouldbe assigned an opportunity contact role of “Executive Sponsor.” Thesystem may determine this by parsing electronic activities sent to andfrom the contact and identify, using NLP, words or a context thatcorresponds to the role of an Executive sponsor. In addition, the systemcan determine if the contact has previously been assigned an opportunitycontact role of executive sponsor in previous deals or opportunities.The system can further determine the contact's title to determine if histitle is senior enough to serve as the Executive sponsor.

In some embodiments, the electronic activity linking engine 250 can usea sequential occurrence of electronic activities to determine contactrecord objects that should be linked or associated with an opportunityrecord object. The electronic activity linking engine 250 can alsodetermine the roles of people associated with the contact record objectslinked to an opportunity. The identification of people associated withopportunity and account record objects (and their associated roles) canbe used to determine stage classification, group of contacts on thebuyer side that are responsible for the purchase, and for many other usecases. In some embodiments, the sequential occurrence of electronicactivities can be used to determine the role or seniority of usersinvolved in a business process. For example, initial emails linked withan opportunity record object can involve relatively lower-levelemployees. Later emails linked to the opportunity record object caninclude relatively higher-level employees, such as managers or VicePresidents. The electronic activity linking engine 250 can also identifythe introduction of contacts in a chain of electronic activities, suchas a series of email replies or meeting invites, to determine acontact's participation and role in a business process. For example, theelectronic activity linking engine 250 can use NLP and other methods toidentify the introduction of a manager as a new OCR based on an emailchain.

It should be appreciated that in some embodiments, the node graphgeneration system 200 can include node profiles corresponding to each ofthe contact record objects included in one or more shadow system ofrecords or master systems of records. As sequential electronicactivities traverse the system 200, the node graph generation system 200can parse the electronic activities and determine that additional emailaddresses are being included or some existing email addresses are beingremoved in subsequent electronic activities. The node graph generationsystem can identify node profiles corresponding to the email addressesbeing added and establish links or relationships between the nodeprofiles included in the electronic activity. As the electronic activitylinking engine 250 links electronic activities to record objects, suchas opportunity record objects, node profiles included in the electronicactivity are also linked to the opportunity record object. The stageclassification engine can use this information to classify a stage ofthe opportunity based in part on node profiles linked to the recordobject and based on the involvement of the node profiles in theelectronic activities that can be determined using effort estimationtechniques, volumes of emails exchanged, as well as based on NLP of thecontent to identify the role of each of the node profiles, as well ashistorical patterns of linkage of similar node profiles to similarrecord objects, as discussed below.

In some embodiments, the electronic activity linking engine 250 can alsodetermine a contact's role based on the tags of the electronic activityin which the contact was included. For example, relatively higher-levelemployees, such as managers, can be more likely to be includedelectronic activities such as in person meeting invites and conferencecalls. The electronic activity linking engine 250 can also use NLP onthe content of electronic activities to determine the role of contacts.For example, the electronic activity linking engine 250 can process thecontent of the electronic activities to identify terms that may indicatea role of a contact. For example, an email can include the phrase “myassistant Jeff will schedule the meeting.” The electronic activitylinking engine 250 can identify the phrase “my assistant Jeff” andinclude in the contact record object associated with Jeff the role of“assistant.” The electronic activity linking engine 250 can alsodetermine that the sender of the email is more likely to be a managerbecause the sender of the email has an assistant.

Similar to how the record object manager 255 maintains the shadowsystems of record and corresponding record objects, the stageclassification engine 325 can maintain a shadow stage indicating a stagethe stage classification engine 325 determines is the current stage forthe deal or opportunity. The stage classification engine 325 candetermine or estimate the stage of the opportunity using a top-downalgorithm or a bottom-up algorithm. With the top-down algorithm, thedata source provider can provide a policy that includes a plurality ofrules. The rules can indicate requirements for entering or exiting astage. For example, the data source provider's policy may include a ruleindicating that an opportunity cannot progress to a negotiation stageuntil a procurement manager is involved in the deal on the buyers side.In this example, the stage classification engine 325 can monitor theingested electronic activities. When the stage classification engine 325detects that the system has linked an electronic activity (such as anemail) to the opportunity record object and the electronic activityincludes a contact that is a procurement manager (as determined, forexample, via the node graph), the stage classification engine 325 canset the shadow stage to negotiation stage. In some embodiments, theshadow stage can be synced to the data source provider's stage for thegiven record object. In some embodiments, the stage classificationengine 325 can update a stage of a record object of the master system ofrecord to match the shadow stage of the corresponding record objectdetermined by the stage classification engine 325. In some suchembodiments, the client may provide or select a configuration settingthat allows the stage classification engine 325 to update the stageclassification of a record object of the master system of record of theclient. In some embodiments, the stage classification engine 325 can usea bottom-up approach to predict or determine the stage. The stageclassification engine 325 can use machine learning to predict ordetermine the stage of a deal or opportunity. For example, the stageclassification engine 325 can combine the features from each of theelectronic activities linked to an opportunity record object into afeature vector. The stage classification engine 325 can use a neuralnetwork, or other machine learning technique, to classify the deal intoone of the stages based on the feature vector. The machine learningalgorithm can be trained using the progression of previous deals throughthe stages. In some embodiments, the stage classification engine 325 canmap the feature vector and plurality of electronic activities to aspecific stage as defined by the data source provider. In someembodiments, the stage classification engine 325 can map the featurevector and plurality of electronic activities to a normalized stage asdefined by the system. The normalized stages can be used with differentdata source providers to provide a translatable staging system ornomenclature across the different data source providers. The stageclassification engine 325 can maintain mappings between the normalizedstages and the stages of the different data source providers. Forexample, the stage classification engine 325 can define five, normalizedstages. A first data source provider can define a deal or opportunity asincluding 7 stages. A second data source provider can define a deal oropportunity as including 3 stages. The stage classification engine 325,for the first data source provider, may map stages 1 and 2 to normalizedstage 1, stage 3 to normalized stage 2, stage 4 to normalized stage 3,stage 5 to normalized stage 4, and stages 6 and 7 to normalized stage 5.Accordingly, the data source provider's stages can be mapped to thenormalized stages based on the tasks, requirements, or content of thestages rather than by the naming or numbering of the stages.

The stage classification engine 325 can map the electronic activities orfeature vector to one of the five normalized stages. The indication ofwhich normalized stage the electronic activities or feature vector wasmapped to can be saved as a shadow stage. When syncing the shadow stageto the master stage of the data source provider, the stageclassification engine 325 can map each of the normalized stages to thestages as defined by the data source provider. For example, the firstnormalized stage may be mapped to the first stage as defined by the datasource provider and the second normalized stage may be mapped to thesecond and third stages as defined by the data source providers.

3. Systems and Methods for Linking Electronic Activities to RecordObjects Maintained on Systems of Record

As described above, the system can maintain one or more shadow systemsof record and shadow stages for each of the data source providers. Theshadow systems of record can mirror the data source provider's systemsof record at different instances in time. In some embodiments, asdescribed above, electronic activities ingested by the system from agiven data source provider are linked to the data source provider'sshadow systems of record to enable the system to perform analysis andgenerate metrics regarding the data source provider's systems of record.In some embodiments, the system can synchronize the linked electronicactivities between the shadow systems of record and the data sourceprovider's master systems of record.

The record object manager 255 can maintain data regarding the recordobjects in the shadow systems of record and the master systems ofrecord. The record object manager 255 can synchronize shadow systems ofrecord and master systems of record for each of the data sourceproviders. In some embodiments, to synchronize the shadow systems ofrecord and the master systems of record the record object manager 255can detect changes in the master systems of record. The changes caninclude added, deleted, or modified account record objects, opportunityrecord objects, or lead record objects or any other record objects. Forexample, the record object manager 255 can determine that a new accountrecord object was generated at the master system of record and generatea corresponding copy of the new account record object at the shadowsystem of record. The corresponding copy of the new account recordobject at the shadow system of record can be a copy of the new accountrecord object at the master system of record. Responsive to adding thenew record object, the system can reprocess previously processedelectronic activities to determine if the electronic activities shouldbe matched with the new record object.

Detecting if modifications occurred to the record objects of the mastersystem of record can include determining if one or more fields of therecord object changed or if the linking of electronic activities withthe record object changed. For example, during a previoussynchronization cycle the record object manager 255 could link anelectronic activity with a first record object at the master system ofrecord. After the synchronization, a user at the master system of recordmay modify linkage to link the electronic activity with a second recordobject. In another example, the system can detect that an additionalfield value was added. For example, location data can be added tolocation field of a record object. The record object manager 255 canresynchronize the updated record object to identify potential newmatches based on the added location data. The system can also reevaluateprevious matches and determine if the location data makes the match withthe previous matches more or less likely. The record object manager 255can determine that the electronic activity was linked by the user to adifferent record object. The record object manager 255 can provide anindication of the change to the record object identification module 315as feedback so that matching model 340 can update its machine learningmodels or matching strategies. In some embodiments, a user can addadditional information or change information in a record object.Responsive to the change to the record object, the system can performthe rematching of the electronic activity with nodes and record objects.

The record object manager 255 can synchronize changes to the shadowsystems of record to the master systems of record. For example, newlinkings of electronic activities to record objects can be synchronizedto the master system of record. Synchronizing the shadow system ofrecord to the master system of record can include adding any linkedelectronic activities since the last synchronization cycle to the mastersystem of record. The electronic activities can be linked to the samerecord object in the master system of record to which they are linked inthe shadow system of record. In some embodiments, the record objectmanager 255 can add a flag or tag to the electronic activity when theelectronic activity is synchronized from the shadow system of record tothe master system of record. The flag can include an indication that theelectronic activity was synchronized from the shadow system of record.In some embodiments, setting of the flag can cause the master system ofrecord to prompt a user of the master system of record to confirm thatthe electronic activity was linked to the correct record object. In someembodiments, setting of the flag can cause the master system of recordto provide a visual indication to a user of the master system of recordthat the flagged electronic activity was linked and synchronized from ashadow system of record. In some embodiments, the user can confirm ordecline the addition of the linked electronic activity from the shadowsystem of record. Based on the approval or disapproval of the linkedelectronic activity, the system can update the matching strategies.

4. Systems and Methods for Generating a Multi-Tenant Master Instance ofSystems of Record Using Single-Tenant Instances

In some embodiments, the system 200 or the system 9300 shown in FIG. 3as described herein can generate a multi-tenant master instance of thesystems of record. The multi-tenant master instance of the systems ofrecord can include data from a plurality of master systems of recordfrom a plurality of different data source providers, which can bereferred to as tenants, or from the plurality of shadow systems ofrecord, which can themselves be mirrors or copies of master systems ofrecord from the different tenants. In some embodiments, the multi-tenantmaster instance of the systems of record can be a combination of therecord objects from the separate shadow systems of record.

As described herein, the system 200 or the system 9300 shown in FIG. 3can include shadow systems of record that correspond to respectivemaster systems of record belonging to respective data source providers.In some embodiments, each of the shadow systems of record (andcorresponding master systems of record) can include a plurality ofrecord objects. The record object manager 255 can synchronize the recordobjects (or data therein) from each of the shadow systems of record ormaster systems of record from different tenants into a multi-tenantmaster instance of the systems of record. As such, the multi-tenantmaster instance of the systems of record can include all of the dataincluded in each record object of the one or more shadow systems ofrecord and the corresponding master systems of record. The multi-tenantmaster instance of the systems of record can be used to further enrichthe node profiles maintained by the node profile manager 220.

The multi-tenant master instance of the systems of record maintained bythe system 200 or the system 9300 shown in FIG. 3 can be used tosynchronize data between the master systems of record from the differenttenants as well as improve the multi-tenant master system of record andindividual master systems of record of the data source providers usingparsed and normalized activity data received from electroniccommunications servers of the data source providers. Moreover, thesystem can update one or more node profiles maintained by the nodeprofile manager 220 using the data from the record objects of the one ormore master systems of record. The record object manager 255 can syncfields or data between node profiles and record objects such as, but notlimited to, names, phone numbers, email address, domains, other contactinformation, address, D-U-Ns numbers, job titles, department IDs andother standard company or person information. In some embodiments, sometypes of systems of record can include record object (and data) typesthat are not included in other types of systems of record such that oneor more of the systems of record may not support all record object typesor data types maintained in the multi-tenant master system or record.

The record object manager 255 can populate data from the record objectsfrom the individual master systems of record into the multi-tenantmaster instance of the systems of record. The record object manager 255can also be configured to synchronize the record objects (or datacontained therein) from the multi-tenant master instance of the systemsof record back to the individual shadow systems of record enabling datato be shared between the different tenants. In some embodiments, eachshadow system of record can include data that is obtained from acorresponding master system of record of a specific data sourceprovider. This data can be shared with or accessed by the record objectmanager 255, which can use the data from each of the shadow systems ofrecord to update the multi-tenant master instance of the systems ofrecord. Moreover, the record object manager 255 can further update therecord objects included in the multi-tenant master instance of thesystems of record from the node profiles of the nodes maintained by thenode profile manager 220. The record object manager 255 can then use thedata included in the multi-tenant master instance of the systems ofrecord, which has been updated from multiple systems of records and thenode profiles, to update one or more of the shadow systems of records,which can then be used to update the corresponding master systems ofrecords of the data source providers.

Data source providers or tenants that provide access to their systems ofrecord can establish, via the system 9300, one or more controls orsettings to manage how the data in their respective systems of recordare treated. In some embodiments, a tenant can select a setting thatrestricts the system 9300 from using the information included in thetenant's system of record to update the master instance of the systemsof record maintained by the system 9300. In some embodiments, a tenantcan select a setting that restricts the system 9300 from using theinformation included in the tenant's system of record to update systemsof record of other tenants maintained by the system 9300. Furthermore,in some embodiments, a tenant can select a setting that restricts thesystem 9300 from using only certain information, such as sensitive orcompetitive information included in the tenant's system of record toupdate the master instance of the systems of record maintained by thesystem 9300. The system 9300 can provide individual tenants control asto how the data included in a tenant's system of record can be updated,used and shared. For instance, a tenant can select a configurationsetting that restricts the system 9300 from updating the tenant's systemof record.

Each record object can include a plurality of fields that are populatedwith data regarding a given record object. As one example, a contactrecord object can include fields for first name, last name, email,mobile phone number, office phone number, among others. A user canpopulate the fields of the contact record object at the master system ofrecord of one of the tenants (e.g., one of the data source providers).The record object manager 255 can synchronize the populated fields intothe corresponding fields of the record object in the shadow system ofrecord. The node profile manager 220, described herein, can generate afirst node (e.g., a member node). The node profile manager 220 canpopulate the fields of the first node with the data from the contactrecord object. In this example, a second user can populate the fields ofa second contact record object in a second master system of record of adifferent tenant. Once synchronized to the system, the node profilemanager 220 can generate a second node based on the second recordobject. In some embodiments, the node resolution engine 245 candetermine that the first node and the second node are associated withthe same contact. For example, the node resolution engine 245 candetermine that the email fields of the first and second nodes arepopulated with the same email address. Determining that the first andsecond nodes are associated with the same contact, the node resolutionengine 245 can merge the first and second nodes such that the mergednode includes data from both the first and the second nodes. The recordobject manager 255 can sync the merged fields back to the respectiverecord objects and master systems of record.

For example, and continuing the above example, the first user may haveentered a phone number into a contact field but not a departmentidentifier into a department field of the first user's respectivecontact record object. The second user may have entered the departmentidentifier into the department field but not the phone number into thesecond user's respective contact record object. The record objectmanager 255 can determine the two contact record objects are associatedwith the same person and merge the data into the multi-tenant masterinstance of the systems of record maintained by the system 200. In someembodiments, the node profile manager 220 can generate a node for theperson in the node graph. To sync or otherwise update the merged databack to the respective contact record objects in the correspondingshadow system of record or the corresponding master system of record,the record object manager 255 can update the first user's contact recordobject with the department identifier and the second user's contactrecord object with the phone number. In some embodiments, the recordobject manager 255 can set a flag indicating the multi-tenant masterinstance of the systems of record as the source of the updated data inthe record objects.

When syncing data between the different tenant systems of record and themulti-tenant master instance of the systems of record, the record objectmanager 255 can resolve conflicts between record objects and fieldvalues in the different systems of record that include different data.The record object manager 255 can resolve the conflicts using theabove-described node graph. For example, the record object manager 255can select between conflicting data by selecting the data that hashighest likelihood of being accurate. The system 200 can, via the nodeprofile manager 220, maintain confidence scores of different values offields to determine a likelihood of the value being accurate. In someembodiments, two values of the same field may both be accurate exceptone may be more current than the other. In such embodiments, the recordobject manager 255 can select the value that is accurate and morecurrent. As described herein, a confidence score of a value can be basedon contribution scores of one or more data points serving as evidencefor the value. The contribution scores of the data points can be basedin part on a recency of the data point and a trust score of the sourceindicating how trustworthy the source is. The trustworthiness of asource, such as a system of record, can be based on a health score ofthe source, which can be determined based on how many values of recordobjects of the system of record match values the system 200 knows to betrue or accurate and how many values of the record objects do not matchvalues the system 200 knows to be true or accurate.

The record object manager 255 can also resolve conflicts based on thetime series of the data for the respective fields. For example, an emailfield that was recently updated by a user may indicate that the contactrecently changed their email address and that the newer email address isan updated email address and not an inaccurate email address.Furthermore, such data may be re-confirmed by extracting the newer emailaddress from an email signature in an electronic activity received froman electronic communications server associated with one of the datasource providers. In some embodiments, the record object manager 255 canperiodically execute batch jobs to synchronize the shadow and mastersystems of record. For example, each evening the record object manager255 can synchronize the shadow and master systems of record. Whensynchronizing the record objects, the record object manager 255 canreprocess previously synced record objects (and the fields therein) todetermine if the record objects should be updated. For example, based onthe electronic activities processed during the day, the confidence scoreassociated with a value of a field of a record object in the shadowsystem of record may have decreased below a predetermined threshold andthe record object manager 255 can remove the value from the field of therecord object of the shadow system of record during the daily sync.

In some embodiments, the synchronization between from the shadow systemor record to the master system of record can be governed by privacypolicies. For example, electronic activities, record objects, or datacontained therein can be flagged to be labeled as private by the systemor a user and may not be synced to the master system of record or toother tenant systems of record. In some embodiments, for little known orpossibly sensitive data, the system may not sync fields back to systemsof record until the data in the field is identified in a predeterminednumber of systems of record. For example, if a contact record object forJohn Smith from a first tenant lists the cell phone of John Smith, thecell phone number may not be synced to other tenants' master systems ofrecord until the system 200 identifies the cell phone number in thecontact record object of a predetermined number (e.g., 3) of tenantmaster systems of record, meaning that at least 2 other companies,connected to the system 200 also possess the phone number for JohnSmith.

5. Systems and Methods for Monitoring Health of Systems of Record

In some embodiments, the system described herein can be used to monitorthe health of a system of record. The source health scorer 215 canmonitor the health of the system of record and can calculate a healthscore for the system of record. The health score for the system ofrecord can be used to determine or otherwise calculate a trust score forthe system of record.

The health (or health score) of a system of record can provide anindication of the accuracy or completeness of a system of record's data.In some embodiments, the health score can be calculated with respect tothe given system of record. For example, the health score can indicatethat 20% of the records within the system of record are inaccurate. Insome embodiments, the health score can be calculated with respect to theother data processing systems. For example, the health score canindicate that the completeness of the systems of record’ database is inthe 97th percentile when compared to the completeness of other systemsof record.

The health score can be based on the completeness of data in the systemof record and/or the accuracy of the data in the system of record. Forexample, each record object in a system of record can include aplurality of fields. In some embodiments, the completeness of the systemof record can be based on the ratio of the total number of populatedstandard fields to the total number of unpopulated standard fields. Insome other embodiments, the completeness of the system of record can bebased on the ratio of the total number of populated standard andsupplemental fields to the total number of unpopulated standard andsupplemental fields. In some embodiments, fields of record objects insystems of record can be classified as standard fields if they arecommon among different systems or record. Examples of standard fieldscan include company name, company phone number, company address asrecord objects across different systems of records for the same companymay each include this information. Similarly, for record objectsdirected towards individuals, the standard fields can include firstname, last name, work phone number, title as record objects acrossdifferent systems of records for the same individual may each includethis information. Other fields that are not standard fields can includecustom fields or fields that include supplemental information that isnot common across different systems of record can be classified assupplemental fields. Examples of supplemental fields can include fieldssuch as opportunity contact role, years of experience, industry, asthese fields may not be common across multiple systems of record.

In some embodiments, the health score can be based on the total count ofthe fields that are populated or just the total count of the standardfields that are populated. In some embodiments, the health score canalso be based on the accuracy of the data populated into the standardfields. The system can determine the accuracy of the data in thestandard fields by comparing the data to other instances of the data inother systems of record or in the multi-tenant master instance. Forexample, the system can determine that the first tenant system of recordindicates a phone number for a given contact is 555-5555. A second andthird tenant system of record can indicate that the phone number for thegiven contact is 555-4433. The system can determine that the phonenumber in the first tenant system of record is incorrect or not currentbecause more tenants (with health scores satisfying a certain threshold)include the 555-4433 phone number. The accuracy of the data can also bebased on the health score associated with data source from which thedata was received. For example, the phone number may not be changed whencontradicted by a source with a low health score. The accuracy of datacan also be based on electronic activities and the confidence score ofvalues of fields maintained in node profiles of the system 200. Theaccuracy of data included in a system of record can be determined bycomparing data included in the record objects of the system of record toinformation included in corresponding node profiles maintained by thesystem 200. As described above, the node profiles can be updated withinformation extracted from electronic activities, which are unbiased andnot self-reported or manually entered. Based on the comparison of thedata included in the record objects of the system of record and thecorresponding node profiles, the source health scorer can determine ahealth of the system of record. The health score can also be timedependent. For example, the health score can decay with time because thedata in the system of record can become stale if the data is not updatedor not checked. In some embodiments, newer data can have a greaterprobability of being accurate. For example, a newly entered job titlefor a contact may be accurate and indicate a promotion.

In some embodiments, the health score can be based on the links betweenrecord objects. For example, the system of record may require that eachopportunity record object be linked with a least one contact recordobject. In these examples, the data fields within the record objects maybe complete but the source health scorer 215 can reduce the system ofrecord's health score or assign a lower health score to a system ofrecord responsive to determining that the system of record does notinclude proper links between one or more opportunity record objects andcorresponding contact record objects or any other record objects withwhich the one or more opportunity record objects should be linked. Insome embodiments, the source health scorer 215 can base the health scoreon the accuracy of the links between the record objects of the system ofrecord. For example, the system can process the electronic activitiesalready linked to the system of record to perform historical matchingbased on using the techniques described herein to generate predictionsfor linking between the system or record's record objects. If thelinkages between the record objects do not match the predicted matches,the source health scorer 215 can assign the system of record a lowerhealth score.

The source health scorer 215 can also calculate or otherwise determine atrust score for each data point included in an array of a value of anode profile maintained by the system 200 or that contributes towards avalue in a record object maintained by the system 200. The trust scorecan be based on the source of the data point. In some embodiments, thetrust score can be based on a health score of the source of the datapoint. For instance, some systems of record can be better maintainedthan others. The source health scorer 215 can perform a health check ona system of record to compute a health score for the system of record.The health score of the system of record can be used to assign a trustscore. In contrast to data points whose source is a system of record, adata point whose source is an electronic activity ingested by the system200 can have a higher trust score since electronic activities do nothave health related issues as they are not manually input or updated.Systems of record are generally manually input and updated and thereforecan include inaccuracies or may be stale resulting in lower healthscores, and thereby, lower trust scores. In some embodiments, the sourcehealth scorer 215 can assign a trust score of 100% or a maximum ratingto data points derived from electronic activities.

6. Systems and Methods for Generating Recommendations to Improve HealthBased on a Node Graph Generated from Electronic Activity

In some embodiments, the system described herein can makerecommendations based on the health and trust scores associated with asystem of record or data source provider. The source health scorer 215or other components of the system can generate the recommendations basedon metrics of the systems of record, record objects therein, and thetrust and health scores associated with the systems of record.

The source health scorer 215 can determine, for each field type, ofnumber of standard fields not populated with data. For example, thesource health scorer 215 can determine, for a given system of record,that 75% of the contact record objects include domain fields that arenot populated with a website field value. In this case, therecommendation can be that the data source provider should update thedomain fields of the contact record objects. In some occurrences, thesystem can automatically fill in a predetermined percentage of themissing field values in a given system of record to automaticallyimprove the health score of the given system of record. Given asignificant number of systems of record, connected to the multi-tenantsystem of record instance and the source health scorer 215, such asystem can systematically and continuously improve the health scores ofall connected systems of record. Stated in another way, by generating ormaintaining a multi-tenant system of record that can be used to updateone or more master system of records maintained by customers orenterprises, a network of systems of record are created with automateddata entry, thereby allowing each of the master systems of records toget updated. This will result in an improvement in the health andcorresponding health score of each of the master systems of recordthrough the network effect until all of the master systems of record areidentical and, in some embodiments, pristine or perfect.

In some embodiments, the recommendations can indicate to a data sourceprovider that the data within the system of record is stale or out ofdate. For example, if a first company is sold to a second company, thesystem can alert the data source provider to update the company or otherinformation in its systems of record based on the sale of the firstcompany. The recommendations can also include updates to field values,organizational charts, job titles, employment changes, and changes to anorganization, such as mergers and acquisitions.

7. Systems and Methods for Filtering and Database Pruning

At least one aspect of the present disclosure is directed to systems andmethods for filtering and database pruning. For example, the taggingengine 265 can assign tags based on the contents of the electronicactivity, associations of the electronic activity with specific nodes,people, or companies, confidence and trust scores, information in recordobjects, or other information associated with the electronic activity.The rules used by the tagging engine 265 to generate tags can be used byone or more systems or components described herein. In some cases, therules used by the tagging engine 265 to generate tags can generatefilter tags, which can be configured to cause the system to block,delete, remove, drop or redact the electronic activity associated withthe filter tag.

A system, such as the data processing system 9300 depicted in FIG. 3,the node graph generation system 200 depicted in FIG. 4, the taggingengine 265 depicted in FIG. 4, the electronic activity linking engine250 depicted in FIG. 9, or one or more components thereof, may performsignificant computationally extensive processing on various types ofelectronic activities or records as depicted in FIG. 3. Since a largevolume of electronic activities associated with sending or receivingelectronic activities are received by the systems or components depictedin FIG. 3, 4, or 9 in accordance with the process flow 9302 depicted inFIG. 9, it can be challenging to efficiently process such data withoutcausing excessive delay or latency issues. Further, databases associatedwith the systems and components depicted in FIGS. 3, 4 and 9, as well asthird-party databases with which the systems depicted in FIGS. 3, 4 and9 can interface or communicate, may store or maintain records that maybe stale, sensitive, corrupt, erroneous, or otherwise not needed or notwanted. As such, systems and methods of the present technical solutioncan provide filtering at an ingestion step 9307 as depicted in thefunctional flow diagram of FIG. 9302, as well as scrubbing of recordsmaintained in one or more databases, using parsing techniques, rules ormachine learning.

The node graph generation system 200 can, via ingestor 205, receiveelectronic activities. The electronic activities can include, forexample, electronic messages or electronic calendar events andassociated metadata. The ingestor 205 can receive the electronicactivities from one or more data source providers 9350, which caninclude an electronic messaging or mail server. The ingestor 205, uponreceiving the electronic activities, can format the metadata orotherwise manage or manipulate the data to facilitate furtherprocessing. The ingestor 205 can receive the electronic activities inreal-time, asynchronously, on a periodic basis, based on a timeinterval, in a batch process or batch download, or responsive to atrigger of event.

The tagging engine 265 can, using one or more rules, policies, ortechniques, tag the electronic activities such that the filtering engine270 can apply a content filter to the tagged electronic activities todetermine whether to filter out the electronic activity or authorize orapprove the electronic activity for further processing, or redact aportion of the electronic activity. The filtering engine 270 can filterout the electronic activity, which can refer to or include redacting outsensitive or private parts of the electronic communications orpreventing the entire electronic activity (or metadata thereof) frombeing forwarded to another component or memory of the system so that theelectronic activity is prevented or blocked from further processing orstorage. Preventing the electronic activity from being further processedor stored can reduce unnecessary computing resource utilization ormemory utilization as well as prevent sensitive or private informationfrom being carried from systems of record or activity data sources toother systems of record.

The electronic activity parser 210 can provide an alert, tag,notification, label or other indication of the reason the electronicactivity was filtered out, blocked or deleted or redacted. Theindication can indicate the type of filter or rule that triggered orcaused the removal or redaction.

In some embodiments, the tagging engine 265 can tag the electronicactivities with a filter tag based on one or more rules or policies. Thefiltering engine 270 can then filter out the electronic activities basedon the assigned filter tag or cause another system or component tofilter out the electronic activity responsive to the filter tag. Forexample, the tagging engine 265, using technologies such as regularexpressions, pattern recognition or NLP, can tag the electronic activityto cause the filtering engine 270 to block ingestion of the electronicactivities or perform other filtering in downstream systems.

The system 200 can be configured to provide, via the filtering engine270, various types of filtering techniques that may be applied toelectronic activities during ingestion, during processing of theelectronic activities, or when attempting to match electronic activitiesto record objects of shadow system of records or master system ofrecords.

As described herein, the filtering engine 270 can be configured to applydifferent types of filtering techniques. As will be described herein,the filtering engine 270 can apply filters based on the content includedin electronic activities. Such filters may be referred to as contentfilters. The filtering engine 270 can also apply logic based filtersbased on one or more logic based rules for filtering electronicactivities. Such filters may be referred to as logic based filters. Inaddition, the filtering engine 270 may apply filters to restrictmatching of electronic activities to node profiles or restrict matchingof electronic activities to one or more record objects of systems ofrecords. Additional details regarding the different types of filters areprovided herein.

As described herein, the filtering engine 270 can apply variousfiltering techniques at a user specific level, a company level, a systemlevel, among others. These filtering techniques can be controlled byusers, administrators of a company, administrators of the system 200,among others.

A. Content Based Filtering

The filtering engine 270 can be configured to perform content filtering.Content filtering involves performing one or more actions on anelectronic activity based on the content of the electronic activity. Insome embodiments, the actions can include restricting ingestion of theelectronic activity into the system 200. In some embodiments, the actioncan include redacting a portion or all of the content included in theelectronic activity. In some embodiments, the action can includerestricting matching the electronic activity to a node profile orrestricting matching the electronic activity to one or more recordobjects.

As described herein, the tagging engine 265 or electronic activityparser 210 can identify terms, text, content or other information in thebody or metadata of the electronic activity. The tagging engine 265 canthen apply a rule, policy, logic, machine learning algorithm, or naturallanguage processing techniques to assign one or more tags to theelectronic activity based on the identified terms, text, content orother information in the body or metadata of the electronic activity.The tag can include a content filter tag or other type of tag that thefiltering engine 270 can use to perform content filtering. In someembodiments, the tagging engine 265 can be configured to apply a taggingpolicy that uses keywords and NLP to identify portions of electronicactivities that satisfy one or more filtering rules. The tagging engine265 can then tag such electronic activities with appropriate contentfilter tags that the filtering engine 270 can use to either redactportions of the tagged electronic activity, block the entire taggedelectronic activity from being ingested, stored, or otherwise processed.In some embodiments, the filtering engine 270 can be configured to parseelectronic activities to determine if the respective electronic activityincludes any of one or more predetermined keywords, phrases, regexpatterns or content in the electronic activity. Responsive todetermining that the electronic activity includes any of one or morepredetermined keywords, phrases, regex patterns or content in theelectronic activity, the filtering engine 270 can restrict ingestion ofthe electronic activity into the system 200.

In some embodiments, the tagging engine can tag the electronic activitywith a tag indicating that the electronic activity includes sensitiveinformation. In some embodiments, the tagging engine 265 can beconfigured to assign specific content filter tags based on the type ofcontent detected in the electronic activity. For instance, the taggingengine 265 can assign a social security tag responsive to detecting asocial security number (or any other number that matches a regex patterncorresponding to a social security number). In some embodiments, thetagging engine 265 can run one or more algorithms to identify varioustypes of information for which a content filtering rule applies.Accordingly, the tagging engine 265 may determine if the electronicactivity includes content that satisfies a content filtering rule, thetagging engine 265 may assign one or more content filter tags to theelectronic activity indicating that the electronic activity includescontent that may be subject to a content filtering rule. In some suchembodiments, the content filter tag can include additional informationthat the system 200 or the filtering engine 270 can use to determine abasis for why the electronic activity satisfies the content filteringrule.

Based on the type of content filter tag assigned to the electronicactivity, the filtering engine 270 can take one or more actions on theelectronic activity. In some embodiments, the tagging engine 265 can tagthe electronic activity with a content filter tag that the filteringengine 270 can use to determine what action to take on the electronicactivity. For instance, in the example of the tagging engine 265assigning a social security tag responsive to detecting a socialsecurity number (or any other number that matches a regex patterncorresponding to a social security number) in the electronic activity,the filtering engine can be configured to parse the electronic activityto identify the content that matches the regex pattern of the socialsecurity number and can apply a redaction policy to the electronicactivity, causing the filtering engine 270 to redact the number from theelectronic activity. It should be appreciated that the filtering engine270 can redact the content by either obscuring the text with a visualmarker, replacing the numbers with text indicating that the content isredacted, or other techniques for redacting text. The system can beconfigured to determine other types of sensitive information includingcredit card numbers, bank account numbers, date of births, or othersensitive or confidential information for which the filtering engine 270may include one more filtering rules.

In some embodiments, the tag can indicate a type of data or fieldpresent in the electronic activity, such as a social security number orcredit card number, in which case the filtering engine 270 can beconfigured with a policy to redact out sensitive information fromelectronic activities or filter out electronic activities tagged ascontaining credit card numbers or social security numbers or othersensitive or private information or perform any other action.

B. Tag Based Filtering

As described above, the filtering engine 270 can use one or more contentfilters or content filtering policies to filter the electronic activity.In some embodiments, the filtering engine 270 can be configured tofilter electronic activities based on one or more tags assigned by thetagging engine 265. Some of the tags assigned by the tagging engine 265can be used to filter electronic activities, either from ingestion bythe system 200 or from matching or linking the electronic activity torecord objects of one or more systems of record. The tags assigned bythe tagging engine 265 can be used for purposes other than filtering,for instance, for updating node profiles, determining connectionstrengths between nodes, understanding context of electronic activities,among others.

The tagging engine 265 can first tag all electronic activities based onnumerous tagging methods as described herein. Thereafter, the filteringengine 270 can determine or choose to filter content out based on one ormore tags, and otherwise determine or choose to allow further processingor storage of electronic activity based on other tags. The taggingengine 265 can determine to generate, apply or assign a tag, such as afilter tag, based on a regular expression. A regular expression (orregex or regexp) can include a sequence of characters that define apattern. Example regular expressions can be configured to detect creditcard numbers, social security numbers, license numbers, date of birth orany other combination of words or numbers. The tagging engine 265 can beconfigured with a regex for a credit card number. For example, a regexfor a credit card number can be defined as a sequence of 13 to 16digits, with specific digits at the start that identify the card issuer.The tagging engine 265 can be configured with predetermined digits ofcard issuers. The tagging engine 265 can apply the credit card numberdetection technique to the electronic activity (or metadata thereof) todetect or determine whether the electronic activity contains a creditcard number. If the tagging engine 265 determines that the electronicactivity contains a credit card number responsive to applying orsearching for the credit card regex, the tagging engine 265 can apply afilter tag to cause the filtering engine to filter out the electronicactivity or redact out the credit card number.

The tagging engine 265 can determine to apply a filter tag based onpredetermined keywords. The keywords can indicate topics, concept orterms. Keywords can include, for example, “Credit Card No.” or “LicenseNo.” or “SSN” or “SSID”, etc. Keywords for topics to be filtered out caninclude, for example, “medical record”, “health record”, “doctor visit”,etc. The system 200 can use a master list of keywords that can be usedto form a Global Keyword Based Content Filter that can be applied acrossall (new and existing) customers or users of the system 200. It shouldbe appreciated that the system 200 can be configured to generate,maintain, use or otherwise access keyword ontology or one or moremachine learning models trained on keywords, clusters of text or otherdocuments to build the master list of keywords. The content filter canbe global or the content filter can be specific to a customer, user orother category or level. The system 200 can continue to update thisglobal list of keyword based content filter. The system 200 can usefilters based on a natural language processing technique to determine oridentify synonyms, translations into other languages, or relatedkeywords.

As described herein, a filter or filter tag can be applied or generatedfor a type of electronic activity. For example, a type of electronicactivity can be adding a non-human participant (e.g., a room, device,projector, printer, display, etc.) to an electronic meeting event. Thesystem 200 can use a filter to prevent or block further processing on anelectronic activity associated with adding a non-human participant orprevent a non-human participant from being matched or from being createdas a new record in the multi-tenant, shadow, or other systems of record.

C. LOGIC-BASED FILTERING

The filtering engine 270 can be configured to perform logic-basedfiltering in which the filtering engine 270 applies one or morelogic-based rules to filter electronic activities. The logic filter caninclude a set of logic-based rules that can be used to filter electronicactivities. The filtering engine 270 can be configured to execute one ormore logic filtering policies by identifying structured metadata aroundan electronic activity or record object, and then blocking theelectronic activity or record object from being ingested by the system200 based on identifying the structured metadata. In some embodiments,the logic-based filtering can apply one or more rules or heuristics torestrict matching an electronic activity to a node profile or to one ormore record objects of systems of record. In some embodiments, thelogic-based filtering can restrict an electronic activity from beingmatched to a particular record object if the electronic activity wassent by a bot or is sent to a personal email address (such as a gmailaddress or a hotmail address, among others).

In one example, the system 200, an administrator of the data sourceprovider or a user of the system 200 can establish a logic-based filterto restrict ingesting electronic activities that satisfy one or morelogic-based rules. In this example, the administrator of the data sourceprovider can establish a logic-based filter to restrict ingestion ofelectronic activities that relate to one or more predetermined federal,state, or local government agencies, for instance, the CIA, NSA or FBI.The administrator can create one or more logic based rules that restrictthe system 200 from ingesting the electronic activity into the system200 if the electronic activity can be matched to an account type fieldhaving a value of government, or if the electronic activity is sent fromor received by a domain name that matches a contains a domain name thatmatches any of the predetermined federal, state, or local governmentagencies, or if the contents of the email include certain predeterminedcharacter strings (for instance, CIA, NSA or FBI) or if the system 200otherwise determines that the electronic activity is in any possible wayrelated to the CIA, NSA or FBI.

D. Matching Filter

As described above, matching filters can be a type of restrictionstrategy or restriction policy that can be used to restrict electronicactivities from being matched to record objects. These matching filterscan be a part of, include or use one or more restriction strategies toprevent electronic activities from being linked to particular recordobjects or a particular system of record in general. In someembodiments, the matching filter can restrict matching electronicactivities to record objects if the matching score between theelectronic activity and the record object is less than or equal to apredetermined threshold (e.g., 70%, 60%, 50%, etc.). The matching scorecan indicate how closely the electronic activity matches the recordobject.

In some embodiments, users of a system of record can be configured toestablish one or more restriction strategies or matching filters torestrict matching electronic activities to certain record objects. Theuser can be a user associated with the record object. In someembodiments, the user can be an administrator of the system of recordand can establish one or more matching filters that can be used torestrict matching. For instance, an administrator can establish amatching filter that restricts matching electronic activities includinga credit card number to certain record objects. In some embodiments, thematching filters can include multiple rules, which when satisfied,restrict the electronic activity from being matched to a certain recordobject. In some embodiments, the matching filters can be used torestrict electronic activities from being matched to record objects evenif the record object was selected by one or more matching strategies1100 and 1104 as described above.

E. NLP Based Tags and Filtering

The tagging engine 265 can determine to apply a filter tag, or thefiltering engine 270 can determine to perform filtering, based onnatural language processing. Natural language processing can refer toparsing metadata associated with the electronic activity to identify ameaning, concept, topic or other higher level concept associated withthe metadata. Natural language processing techniques or algorithms candetermine whether the electronic activity contains or is regarding aconcept that is to be filtered out.

Natural language processing can be used to determine whether anelectronic activity is a personal electronic activity or a businesselectronic activity. The filtering engine 270 can perform or execute thefilter to redact out sensitive content, block or prevent furtherprocessing of personal electronic activities, and authorize or approvefurther processing of business related electronic activities. Thetagging engine 265 when determining to apply a filter tag, or thefiltering engine 270 when determining whether to perform filtering, candetermine whether the electronic activity is personal based on a tagfrom the tagging engine or an email identifier used by the sender orrecipient (e.g., a domain of the email address indicating personal use(or typically used for personal use) versus a domain indicating abusiness use or corresponding to an employer of the sender orrecipient). The system 200 (e.g., via electronic activity parser 210,tagging engine 265, or filtering engine 270) can further determinepersonal or business electronic activities based on keywords, terms,topics, or concepts of the electronic activity (e.g., vacation-relatedversus order or purchase related). In some cases, the system 200 candetermine that an electronic activity was sent using a personalelectronic mail address, but the content of the electronic activity wasto further a business objective. Thus, the system 200 can initiallydetermine to block the electronic activity, but then determine toauthorize or approve the electronic activity, thereby overriding aninitial filter layer.

The system 200 can use a natural language processing engine that can beconfigured to parse text or keywords in different languages anddetermine synonyms or equivalent concepts across multiple languages. Forexample, the system 200, using the natural language processing engine,can determine the equivalent of a keyword in English but in Japanese,and, therefore, be configured to perform tagging and filtering inmultiple languages. The NLP engine can further expand a keyword into anumber of synonyms or related keywords. Thus, even if the list ofkeywords used for filtering are not comprehensive, the system can stillperform robust tagging and filtering.

F. Machine Learning Based Filtering

The system 200 can use machine learning to determine whether to filteran electronic activity. Machine learning can refer to a training set ofdata that includes metadata for electronic activities that are to beapproved or authorized for further processing, as well as metadata forelectronic activities that are to be filtered out based on bothstructured data and also on vectorized content of the communications.For example, if words in the electronic activities are converted intovectors with word2vec or similar technology, a machine learning modelcan be trained based on the content of the electronic activitiesalongside (not mutually exclusive) with natural language processingsystems. The machine learning based filter can automatically establish,based on the training set, features, weights or other criteria thatindicate whether or how an electronic activity should be tagged. In someembodiments, the system 200 can then determine if the electronicactivity should be approved or filtered out based on the one or moretags. Thus, by using a machine learning technique, the system 200 canautomatically determine features, weights or criteria to detect inmetadata of the electronic activity to determine whether to tag and thenfilter out or authorize the electronic activity for further processing.

G. Filtering Based on Bot Detection

The machine learning filtering technique can include bot detection. Thesystem 200 (e.g., electronic activity parser 210, tagging engine 265 orfiltering engine 270) can use or be configured with a bot detectionmachine learning algorithm to detect whether the electronic activity wassent by a bot—such as an automatic electronic activity generator. If theelectronic activity was transmitted by a bot, the tagging engine 265 orthe filtering engine 270 can tag the electronic activity with a tagindicating that the electronic activity was generated by a bot. In someembodiments, the filtering engine 270 can remove or prevent furtherprocessing or storage of the electronic activity (e.g., responsive tothe tagging engine 265 tagging the electronic activity with a filter tagor a tag indicating that the electronic activity was transmitted by abot). The system 200 can leverage or use the node graph to automatepopulating a blacklist of email addresses or other unique identifiersassociated with bots through bot detection for syncing. For example, ifan electronic activity is from a bot, that information may not bematched, linked or synced to a system of record. In some embodiments,the system 200 can detect bots based on the node graph since the nodegraph can indicate that a node of the sender bot is associated withedges of interactions between other nodes that indicate heavy one-wayinteractions across a large number of nodes. Thus, by measuring ordetecting edge connection strength between the sender bot node and othernodes, or in some embodiments, comparing the number of inboundinteractions (received electronic activities) and outbound interactions(transmitted electronic activities) between the sender bot node andother nodes, the system can classify non-human participants. Forinstance, non-human participants, such as no-reply@example.com generallytransmit emails to a large number of other nodes but only receive a muchsmaller number of emails from other nodes, thereby allowing the system200 to classify the email no-reply@example.com as a bot. The system 200can use or apply similar techniques to detect and classify other typesof non-human communication patterns and activity participants. Forexample, conference room email addresses only get added to meetings butnever send or receive regular (non-meeting invite) emails and thus canbe classified as Conference Room bots. Furthermore, the system 200 canidentify a bot by parsing the email address or name associated with theemail address. For instance, the bot detection algorithm can detect thatan email address including “no-reply” is associated with a bot. In someembodiments, the bot detection algorithm can parse an electronicactivity and determine, using NLP, that the email address associatedwith the sender is a bot based on language indicating not to send areply to the electronic activity.

H. Using Feedback to Improve Filtering Techniques

The system 200 can tune or improve the machine learning techniques basedon feedback. For example, upon applying the machine learning techniquesto electronic activities, the system 200 can provide the filter decisionto an administrator or other user of system 200. The user can inputwhether the filter decision was correct or incorrect. If the filterdecision was correct, the machine learning filter can maintain theweights or rules used to make the filter decision, or increase weightsused to make the filter decision. If the filter decision was incorrect,the system 200 can modify the features, weights or criteria in anattempt to correct the filter decision. Similarly, the system 200 canuse user input to modify features, weights, or criteria for other typesof tagging or filtering, including, for example, natural languageprocessing, rules, linking, or other logic flows that can be improved,enhanced or otherwise benefit from user input. In some embodiments, themachine can be configured to update the weights based on feedbackwithout any intervention of a user or administrator.

I. Global Pattern Based Process Filter

Since end users can send/receive sensitive information from varioussystems (e.g., Human Resource systems, payroll systems, benefitssystems, applicant tracking systems, recruiting systems, medical billpayment systems, phone bill payment systems, utility bill paymentsystems, banking or financial institutions, ride sharing systems, etc.)that could include highly sensitive information like payroll, benefits,hiring/termination letters, feedback on hiring a candidate or a cellphone bill which includes call details, the filtering engine 270 canblock or prevent such electronic activities from being further processedor ingested by one or more components of the node graph generationsystem 200 or electronic activity linking engine 250 by maintaining aGlobal Pattern Based Process Filter which is applicable for all nodes.This can include a pattern based process filter that can identify rulesto detect automated emails based on a ‘from’ and other fields; trigger ajob to generate an automated systems blacklist; obtain a globalblacklist from a storage or database; and apply a global pattern basedprocess to filter out automated system electronic activity. Patterns canalso be based on or applied to different fields or aspects of electronicactivities, such as other data in an electronic message, meeting orcalendar entry, telephone transcript, etc.

To generate such filters, the tagging engine 265 or filtering engine 270can use bot detection techniques to identify bots automatically andblacklist the identified bots. Further, and in some embodiments, thetagging engine 265 can use natural language processing or machinelearning techniques to automatically assess sensitivity of data from anew sender. For example, if a new source starts sending emails tomultiple users, and greater than a threshold percentage (e.g., 70%, 80%,90% or some other percentage) of the emails contain sensitive orconfidential information (e.g., social security numbers), then thesystem 200 can automatically generate and apply a global filter toautomatically blacklist this source.

J. Hierarchy of Filtering Rules

The system 200 (e.g., via electronic activity parser 210, tagging engine265 or filtering engine 270) can apply one or more layers of filters.The system 200 can apply the one or more layers of filters in parallelor serially. The system 200 can select one or more layers of filters toapply to an electronic activity based on a policy, rule or other logic.Layers of filters can refer to or include different types of filters ordifferent configurations for filters. Layers of filters can refer to orinclude different type of filter controls or thresholds. Layers offilters can correspond to a hierarchy. For example, a first filter layercan include filtering policies, rules or logic established, based on orcustomized for a node in the node graph (e.g., a member node, anemployee node, a user node, or an individual node). A second filterlayer can include filtering policies, rules or logic established, basedon or customized for an account. The account can refer to buyer accountestablished by a seller for the buyer. A third filter layer can includefiltering policies, rules or logic established, based on or customizedfor an organization. The organization can refer to or include a buyerorganization, such as a company. A fourth filter layer can includefiltering policies, rules or logic established by governmental agencies.A fifth filter layer (or master filter layer) can include filteringpolicies, rules or logic established, based on or customized for anadministrator or provider of the node graph generation system 200 orelectronic activity linking engine 250. Different entities can establishvarious types of filters with various thresholds, controls, rules orpolicies.

As described below, the system 200 can be configured to apply differenttypes of filtering policies. Each of the filtering policies outlinedbelow can correspond to one of the filter layers described above.

K. Entity-Defined Filtering Policies

The system 200 can select one or more filters to apply to an electronicactivity or all electronic activity ingested. The system 200 can selectthe one or more filters based on the metadata associated with theelectronic activity. The system 200 can select the one or more filtersto apply based on filtering rules defined for an account, a user, agroup of users within an enterprise, an enterprise, or the system 200.

i. Account-Specific Filtering Policies

The filtering engine 270 can maintain account-specific filteringpolicies that include one or more rules defined for one or moreaccounts. For instance, the filtering engine 270 can be configured toapply filters to emails either transmitted by or received by a specificaccount, such as an email account. In some embodiments, theaccount-specific filtering policy can include one or more rules to applyone or more content filters, logic-based filters or matching filters onelectronic activities corresponding to the specific account. Forinstance, the filtering engine 270 can apply an account-specificfiltering policy to an account, such as an email address correspondingto a bot. In one example, the account-specific filtering policy caninclude a rule to restrict matching any emails transmitted by theaccount to a record object of a system of record. The account-specificfiltering policy can be defined by a user, an administrator of theenterprise, or an administrator of the system 200.

ii. User-Specific Filtering Policies

The filtering engine 270 can maintain user-specific filtering policiesthat include one or more rules defined for specific users. For instance,the filtering engine 270 can be configured to apply filters to emailseither transmitted by or received by a specific user. In someembodiments, the user-specific filtering policy can include one or morerules to apply one or more content filters, logic-based filters ormatching filters on electronic activities corresponding to the specificuser. For instance, the filtering engine 270 can apply a user-specificfiltering policy that includes restricting certain electronic activitiessent by the user from being linked to one or more systems of record orto the node profile of the user. For instance, the user may define arule to restrict any emails between the user and their lawyer from beinglinked to one or more systems of record or to the node profile of theuser. In another example, the user may define a rule to restrict emailssent to the user's spouse at a given company to be linked to recordobjects of the company. The user-specific filtering policy of a user canbe defined by a user, an administrator of the enterprise, or anadministrator of the system 200.

iii. Group-Specific Filtering Policies

The filtering engine 270 can maintain group-specific filtering policiesthat include one or more rules defined for specific groups of users. Forinstance, the filtering engine 270 can be configured to apply filters toemails either transmitted by or received by users defined within aspecific group of users. In some embodiments, the group-specificfiltering policy can include one or more rules to apply one or morecontent filters, logic-based filters or matching filters on electronicactivities corresponding to the specific group of users. For instance,the filtering engine 270 can apply a group-specific filtering policythat includes restricting certain electronic activities sent by a userof the group from being linked to one or more systems of record or tothe node profile of the user. For instance, a user of the group or anadministrator of the enterprise associated with the group or the system200 may define a rule to redact text included in any emails between oneor more users of the group before storing the electronic activity in thesystem 200 or any record object with which the electronic activity ismatched. The group-specific filtering policy of a user can be defined bya user, an administrator of the enterprise, or an administrator of thesystem 200.

iv. Enterprise-Specific Filtering Policies

The system 200 can select the one or more filters to apply based onenterprise-specific filtering policies. For example, an administrator ofa first enterprise or customer of the system 200 can indicate to removeelectronic activity accessed via a mail server of the first customerhaving metadata that matches a regex for a credit card number, whereasan administrator of a second enterprise or customer of the system 200can indicate to remove electronic activity accessed via a mail server ofthe second customer having metadata that matches a keyword or regexpattern for a credit card number. In this example, the system 200applies enterprise-level filtering rules that may be defined by specificenterprises on how to process electronic activities received from theirelectronic communications servers. The enterprise-specific filteringpolicy of a user can be defined by an administrator of the enterprise orthe system 200.

v. System Defined Filtering Policies

The filtering engine 270 can maintain system-specific filtering policiesthat include one or more rules defined for the system 200. For instance,the filtering engine 270 can be configured to apply filters to allelectronic activities processed by the system 200. For instance, thesystem-specific filtering policy can include one or more rules to applyone or more content filters, logic-based filters or matching filters onelectronic activities accessible by or ingested by the system 200. Forinstance, the filtering engine 270 can apply a system-specific filteringpolicy that includes tagging all electronic activities that includenumbers matching a credit card regex pattern with a credit card tagindicating that the electronic activity includes a credit card. Thesystem 200 can then be configured to determine if the filtering engine270 is to take any additional actions on the electronic activity withthe credit card tag, for example, redacting the credit card information,restricting matching the electronic activity to record objects, amongothers. The system-specific filtering policy can be defined by anadministrator of the system 200.

L. Sensitive Information Filter

The system 200 can determine to filter out an electronic activityresponsive to detecting sensitive information or data in or associatedwith the electronic activity. For example, upon detecting or identifyinga social security number or a financial account number in the electronicactivity, or a tag indicating sensitive information, the system 200 canfilter out the electronic activity. This is an example of one type ofcontent filtering.

M. Source Based Filtering

The system 200 can select the filter to apply based on who is a senderor recipient of the electronic activity or a source of the electronicactivity (for example, whose mail server the electronic activity camefrom). In some cases, the system 200 can select one or more filters toapply or apply all filters that are configured or compatible with theelectronic activity. This is similar to the user-specific filteringpolicy described above. In one example, the data source provider of asystem of record can establish a rule that causes the system 200 torestrict any emails coming from a craigslist.org domain from beingmatched to any record object of the system of record.

N. Previous Communication Activity Filter

The system 200 can filter out the electronic activity if there have beenno previous electronic activities between the sender and recipient ofthe electronic activity. However, the system 200 can authorize orapprove the electronic activity if electronic activities have occurredbetween a node in close proximity in the node graph to the sender nodeor recipient node. In some embodiments, two nodes may be in closeproximity in the node graph if they have a connection strength above apredetermined threshold. In some embodiments, two nodes may be in closeproximity in the node graph if they have either exchanged electronicactivity with each other or with a predetermined number of connectionnodes in common. Thus, the system 200 can determine to approve or filterout electronic activities based on the extent to which prior electronicactivities between the sender and recipient have occurred (e.g., ametric associated with one or more prior activities satisfying athreshold).

O. Geographic Location Based Filters

The system 200 can select filters based on a geographic locationinferred for a node associated with the electronic activity. In someembodiments, a geographic location can be inferred for a node based ondetecting a time zone based on timestamps of electronic activitiestransmitted by the sender node. In some embodiments, the filteringengine 270 can be configured to apply one or more filtering rules basedon a geographic location of the sender or recipient of the electronicactivity. In some embodiments, geographic location based filtering maybe applied in conjunction with the previous communication activityfilter or other types of filters. For instance, the system 200 can beconfigured to restrict matching electronic activities sent to a firstuser from a second user if the number of communications between the twousers is less than a certain threshold and the first user is in aparticular geographic region, for instance, Massachusetts.

In some embodiments, the system 200 can authorize or approve furtherprocessing or storage of electronic activities if a user has consentedto the further processing or storage of such electronic activities. Thesystem 200 can authorize or approve further processing or storage ofelectronic activities if, based on the overall volume or nature ofcommunications, as well as keywords or context, the metadata associatedwith the electronic activity indicates an opportunity, contract, orother relationship. The system 200 can determine, using the overallvolume, context and nature of electronic activities, (which can bedetermined using keywords, machine learning or natural languageprocessing), whether the electronic activity is indicative of alegitimate business interaction (e.g., amount of time spent onelectronic activities, number of electronic activities, roles, or typeof electronic activity such as in-person, video conference, webconference, telephone call). Responsive to this determination, thesystem 200 can authorize or approve the electronic activity for furtherprocessing, or conversely delete, remove or block further processing orstorage of the electronic activity if the electronic activity is notindicative of a legitimate business interaction.

Using the filters, the system 200 can determine which electronicactivities are to be processed by the system or added, stored or linkedto nodes in a node graph or a system of record or one or more systems ofrecord. As described herein, the filtering techniques can be used toprevent sensitive or private electronic activities from being linked orstored in a system of record. The filter can work by (1) completelyfiltering out the electronic activity, or (2) filtering out/blockingingestions of the content of the electronic activity, while theelectronic activity itself, without the content body, is synced and agraph edge between the sender and recipient of the electronic activityis created, or (3) by redacting out sensitive parts of the electronicactivity.

In some cases, the filtering engine 270 can be configured to apply oneor more filtering policies to one or more systems of record to scrub orremove data from the systems of record that satisfy the one or morefiltering policies. For example, a system of record of an enterprise canbe pruned using one or more enterprise-specific filters to removeelectronic activities or other values or data from the system of recordof the enterprise that satisfy the one or more enterprise-specificfilters. For instance, an administrator of an enterprise can establish anew filtering policy to redact social security numbers from anyelectronic activities that include social security numbers and that arealso matched to the systems of records of the enterprise. The filteringengine 270 can be configured to evaluate, responsive to the newfiltering policy, each electronic activity previously matched to thesystems of record of the enterprise to identify if the electronicactivity includes a social security number and if so, redact the socialsecurity number from the electronic activity.

It should be appreciated that the filtering engine 270 can be configuredto apply one or more filtering policies defined by a user of the system,an administrator of the enterprise or an administrator of the system 200to prune one or more systems of record, a shadow system of record, or amaster system of record.

In some embodiments, the system 200 can identify electronic activitiesor record objects having personal email domains. The system 200 canmaintain a static list of all personal email domains to determine whichdomains are personal and which domains are not personal. However, toprevent personal electronic activity from being matched or synced to thesystem of record, the filtering engine 270 can allow linking and syncingbased on the domain name.

8. Systems and Methods for Threshold-Based Data Management

At least one aspect of the present disclosure is directed to systems andmethods for threshold-based data management. The system 200, such as thetagging engine 265, filtering engine 270, electronic activity parser 210or other component or module, can analyze a data source provider'ssystem of record to identify with which contacts or nodes, employees orusers associated with the data source provider have sufficient activityabove a predetermined level or threshold. Responsive to the system 200determining that the level of activity between a user associated withthe data source provider and a contact or node is equal to or greaterthan (i.e. satisfies) a predetermined threshold, the system 200 canauthorize, allow, or approve for storage an electronic activity betweenthe user associated with the data source provider and the contact ornode. For example, if the user associated with the data source providerhas communicated with the contact or node before or had a certain numberof communications or certain number of communications of a certain typeor certain context, then the system 200 can determine that level ofinteraction satisfies a threshold and proceed to store the electronicactivity or metadata or other information associated with the electronicactivity or the node or contact. In this way, cold emails, unsolicitedemails or other types of electronic activities that do not warrant beinglinked to the system of record of the data source provider can berestricted from being linked to the system of record of the data sourceprovider.

The system 200 can detect, using keywords, machine learning or naturallanguage processing, whether the electronic activity is indicative of alegitimate business interaction based on the volume, nature, content orcontext of the electronic activity or based on the number of electronicactivities transmitted between the user associated with the data sourceprovider and the contact. For example, the system 200 can detect alegitimate business interaction based on the amount of time the contactor user associated with the data source provider spent on electronicactivities. In some embodiments, the system 200 can detect a legitimatebusiness interaction based on a number of electronic activities, roles,direction of the electronic activity such as inbound or outbound, ortype of electronic activity such as in-person, video conference, webconference, or telephone call. Responsive to determining whether theelectronic activity is indicative of a legitimate business interest, thesystem 200 can authorize or approve the electronic activity for furtherprocessing, or otherwise delete, remove or block further processing orstorage of the electronic activity.

In some embodiments, the system 200 can leverage a node graph todetermine the level of activity between an employee associated with thesystem of record and a contact. The system 200 can determine the levelof activity based on the number of electronic activities transmitted bythe employee to the contact, the number of electronic activitiestransmitted by the contact to the employee, or the type of electronicactivities being transmitted, information associated with the electronicactivities (e.g., calendar invite for a teleconference or in-personmeeting, blast email, etc.). In some embodiments, the system 200 canauthorize or approve the electronic activity if electronic activitieshave occurred between a node in close proximity in the node graph to thesender node or recipient node. In some embodiments, two nodes may be inclose proximity in the node graph if they have a connection strengthabove a predetermined threshold. In some embodiments, two nodes may bein close proximity in the node graph if they have either exchangedelectronic activities with each other or with a predetermined number ofcommon nodes. Thus, the system 200 can determine to approve or filterout electronic activities based on whether prior electronic activitiesbetween the sender and recipient have occurred, or the extent to whichprior electronic activities between the sender and recipient haveoccurred (e.g., a metric associated with one or more prior activitiessatisfying a threshold).

In some embodiments, the system 200 can scan or crawl the content ofelectronic activities received from a contact or node to detect proof ofconsent or other indication of interest (e.g., detecting a type ofintent) using natural language processing. If the system 200 determines,based on the content of the electronic activities, that the contactgives permission to store data associated with the contact, then thesystem 200 can proceed to store the data associated with the contact.

As described above, the system 200 can be configured to determine ifelectronic activities are personal or business, and through suchelectronic activities between two nodes, classifying the relationshipbetween the two nodes as either personal or business (and then taggingsuch relationship as personal/business and then other parts of system200 can use such tags to perform one or more functions or actions,including filtering, matching, among others).

In some embodiments, certain companies may establish one or more rulesto limit the initiation of communications from an employee of thecompany to other nodes or people outside of the company. The system 200can be configured to assist such companies by identifying contacts ofthe employees that the employee may be allowed to contact based on theemployee's previous electronic activities with other nodes or people orbased on certain types of introductions.

To do so, the system can be configured to detect business orprofessional introductions from electronic activities. In someembodiments, the system can be configured to determine if an electronicactivity, using NLP or other techniques, whether an electronic activitycan be tagged with an “introductory” tag. The tagging engine 265 candetermine if an electronic activity or a sequence of electronicactivities should be tagged with an introductory tag responsive todetermining that the context of the electronic activity is one thatrelates to an introduction. The system 200 can then determine theparticipants of the electronic activity and create a tag or anindication in their respective node profiles or elsewhere in the system200 that the participants have been introduced and qualify as contacts.Upon qualifying the participant as a contact of the other participant,if the other participant is an employee of a company that employs ruleslimiting the types of people the employee can contact, the system 200can identify the participant as a person the employee can contact. Insome embodiments in which the system 200 can update one or more systemsof the company, the system can provide an indication to the system thatthe employee is authorized to contact that person now that the person isa contact of the employee.

9. Systems and Methods for Maintaining an Electronic Activity DerivedMember Node Network

At least one aspect of the present disclosure is directed to systems andmethods for maintaining an electronic activity derived member nodenetwork. For example, a member node profile for a member node in a nodegraph can include information such as first name, last name, companyname, phone number, email address, and job title, among others. However,it may be challenging to accurately and efficiently populate fields in amember node profile due to large number of member nodes who may changecompanies, get promotions, change names (for instance via marriage, orchange locations, among others. Furthermore, permitting self-reportingon information in member node profiles by member nodes can result inerroneous data values, improper data values, or otherwise undesired datavalues. Having erroneous data values in a member node profile that areunsubstantiated by data points serving as evidence to a value of a nodeprofile can cause downstream components or functions that performprocessing using the member node profiles to malfunction or generatefaulty outputs.

Thus, systems and methods of the present disclosure can generate anelectronic activity derived member node network that includes membernode profiles for member nodes that are generated or updated based onelectronic activity processed by the system. By generating the membernode profiles for the member nodes using electronic activities orsystems of records and a statistical analysis, the system 200 can updatemember node profiles using electronic activities, record objects ofsystems of record and other data points as described above. The system200 can generate or update member node profiles using data included insystems of record of data source providers, and validate values includedin the member node profile using electronic activities and recordobjects and a statistical analysis.

Furthermore, the node graph generation system 200 can further establishlinks, connections or relationships between member node profiles basedon electronic activities exchanged between them or other electronicactivities processed by the node graph generation system 200. Theseestablished links, connections or relationships and the correspondingnode profiles form the node graph generated by the node graph generationsystem 200.

By generating the member node profiles and the corresponding node graphby processing electronic activities traversing through or beingprocessed by the node graph generation system 200 and accessinginformation included in one or more systems of record, the node graphgeneration system 200 can generate the member node profiles using astatistics-driven analytics process based on the electronic activities,thereby improving upon existing node graphs that are generated based onself-reported information by users. Such existing node graphs are notdynamically updated automatically based on electronic activities and mayinclude information that is inaccurate or not vetted as the informationis self-reported with no or little verification.

Furthermore, as the node graph generated by the system 200 is generatedin part using electronic activities that are continually being generatedand transmitted, the node graph can remain current and up to datewithout requiring any self-reporting on the part of the nodes associatedwith the node profiles. Furthermore, given that the node graph isupdated as more electronic activities are generated, the system can,using certain parameters, such as dates, be able to determine a statusof the node graph at any particular point in time. This is because thenode graph is generated, in part, based on electronic activities thatare time-stamped and as such, electronic activities that occur beforethe particular point in time can be used to determine the status of thenode graph while electronic activities occurring after the particularpoint in time can be discarded from the analysis relating to determiningthe status of the node graph (and individual node profiles) at theparticular point in time.

To generate the member node profiles, the node graph generation system200, or components thereof such as the node profile manager 220, canreceive electronic activities including any information related to theelectronic activities. The node graph generation system 200 can maintainan array of a time series data set of data points or sources for everyvalue of every field, parameter, or attribute of every node. As alsodescribed above with respect to the node profile manager 220, the nodegraph generation system 200 can associate node profiles of the nodegraph to electronic activities, or update node profiles that form thenode graph based on updates detected by the system 200 responsive toparsing the electronic activities. The node graph generation system 200can automatically detect potential changes to fields of node profiles ofthe node graph based on patterns in the electronic activities, and thendetermine to trigger an update to the node graph. The node graphgeneration system 200 can sync with one or more systems of records todetermine additional information that can be used to update one or moremember node profiles. As described herein, updating a node profile doesnot necessarily mean changing a value of a node profile. In someembodiments, updating the node profile can include adding additionaldata points to a value data structure to increase or adjust a confidencescore of a value corresponding to the value data structure. In someembodiments, the data points can be electronic activities. In someembodiments, the data points can be values determined from recordobjects of one or more master systems of record. In some embodiments,the system 200 can receive information from record objects of one ormore systems of record and use the information to create new nodeprofiles or update existing node profiles by adding data points tosupport values of fields of such node profiles.

Each node profile can include values that are based on one or more datapoints. The system is configured to determine, for a particular time, astate of any node profile. The state of the node profile at any giventime can be a representation of the node profile using electronicactivities and systems of record data that occurred prior to the giventime. For instance, the system is configured to output a job title of agiven node at a particular date, for example, Dec. 2, 2017. The systemcan do so by discarding any electronic activity generated after Dec. 2,2017 and any data from a system of record that was modified after Dec.2, 2017.

Similarly, the system can be configured to detect changes to a nodeprofile and generate a timeline of changes to values of fields of thenode profile. For instance, the system can be configured to detect thata node has changed jobs or gets a new title, among others, based onmonitoring electronic activities accessible to the system. For instance,the system can determine that a node has changed jobs if the systemdetects bounce back activity from the email address of the node and alsodetects that a person with the same name, phone number in the emailsignature (or other values) as the node is sending emails from a newemail address, perhaps, around the same time that the system detectsbounce back email activity from the email address of the node.Similarly, the system can detect a change in the job title based on achange in a signature of the node. The system can then identify a datethat the signature was first changed to reflect the new title and markthat date as a date of the title change. In this way, the system candetect when users or nodes get promotions, demotions, join newdivisions, leave jobs, start new jobs, among others.

In some embodiments, the system 200 can be configured to providecompanies access to data collected, generated and managed by the system200. The data managed by the system 200 can be used to provide insightsto the companies, improve the accuracy of data maintained in one or moresystems of record of the companies, among others. In some embodiments,the companies that receive access to the data managed by the system 200can provide access to data maintained by one or more systems of recordof the company as well as electronic communications servers (forexample, email servers, messaging servers, among others) of the company,phone servers of the company, as well as other data sources maintainedor under the control of the company.

Upon a company providing access to the servers storing data of thecompany to the system 200, the system 200 can be configured to establishone or more communication interfaces with the one or more serversstoring the company's data. The servers storing the company's data caninclude the email servers, messaging servers, the systems of recordservers, among others. Upon establishing communication interfaces withthese servers, the system 200 can be configured to receive data fromeach of the servers storing the company's data. The system 200 caningest the data and process it as described with respect to FIG. 3 andothers.

In some embodiments, the system 200 can receive a large number ofelectronic activities from the electronic communication servers storingelectronic activities of the company. These electronic activities caninclude all electronic activities accessible by the electroniccommunication servers. Some of the electronic activities received can beemails that were sent many years ago. However, such electronicactivities can still be processed by the system 200 even thoughelectronic activities from other electronic communication servers thatwere generated more recently have previously been processed by thesystem 200.

Similarly, the system 200 can receive data from one or more systems ofrecord of the company. The systems of record can include record objectsthat include values of fields. The system 200 can be configured toingest the data from these record objects of the systems of record andprocess the data included in the record objects.

The system 200 can be configured to process these electronic activitiesand record objects by updating one or more node profiles maintained bythe system 200 or generating new node profiles responsive to determiningthat certain electronic activity or record objects do not match anyexisting node profile with a certain minimum level of confidence. Thesystem can be configured to determine, for each electronic activityingested by the system 200, whether the electronic activity can be usedas evidence to support any value of a field of any existing node profilemaintained by the system 200. The system can do so by attempting tomatch the electronic activity to node profiles of the system 200.Responsive to identifying a node profile with which to match theelectronic activity, the system can add the electronic activity as adata point to a value of the field of the node profile that was used tomatch the node profile with the electronic activity. Similarly, thesystem can match record objects to node profiles by matching values ofnode profiles to values of existing node profiles. Once a node profileis matched with an electronic activity or record object, the system candetermine if there are any values included in the electronic activity orrecord object that does not previously exist in the node profile. If so,the system can add a value to a corresponding field of the node profileand add the electronic activity or record object as a data pointsupporting the added value.

It should be appreciated that the system 200 can be configured to ingestand process each and every electronic activity maintained by theelectronic communication servers under the control or direction of thecompany as well as each and every record object maintained in one ormore systems of record of the company. As such, a large amount ofelectronic activity and record objects are processed and can be used toupdate existing node profiles maintained by the system 200 or generatenew node profiles for the system 200. As additional companies shareaccess to their data with the system 200 and the system 200 processesthe data, the node profiles maintained by the system 200 will be furtherenriched and the data included in the node profiles will be moreaccurate. Moreover, data that is less accurate will have lowerconfidence scores while data that is more accurate will have higherconfidence scores as there will be more data points that will becontributing towards the confidence score of the correct values. In thisway, the node profiles will become more accurate. As a result, as morecompanies are on boarded and share access to their data with the system200, the node graph generated from the node profiles will also becomemore accurate further increasing the accuracy of the system and each ofthe node profiles and corresponding node graph. For example, the nodegraph generation system 200 can detect a change in an electronic mailaddress status responsive to an electronic message bouncing back due tothe message being undeliverable or otherwise not deliverable to the sentaddress, or having an automated “no longer with company” auto-responder.The node graph generation system 200 can further detect information fromthe electronic activities or the one or more systems of record withwhich the node graph generation system 200 (or electronic activitylinking engine 250) interacts in order to obtain, infer or determineadditional information that can be added to the member node profile. Byparsing data from a bounce back electronic activity or auto-respondergenerated electronic activity, the system 200 can determine variouspieces of information. For example, by applying natural languageprocessing to auto-responder generated electronic activities, the systemcan detect different events corresponding to a node profile associatedwith the email address for which the auto-responder generated electronicactivity was generated. In one example, the autoresponder generatedelectronic activity can indicate that the person is on a vacation. Suchautoresponder generated electronic activities can either mention theword “vacation” or some other synonym or words that may suggest avacation. The electronic activity can also identify a return dateindicating a date when the person will return to the office. Theelectronic activity can also identify another person (along with anemail address, title and phone number, if present) to contact while theperson is on vacation. The system can be configured to update the nodeprofile of the person to indicate that the person is out of the officeuntil the return date and further update the node profiles of the personand the other person to indicate a connection or relationship betweenthem. The system can learn from the autoresponder generated electronicactivity to determine who else to talk to at the company on a specificmatter while the person is on vacation. Moreover, the system can monitordifferent autoresponder generated electronic activities generatedresponsive to the same email address to determine other connections ofthe person if different autoresponder generated electronic activitiesinclude different people. Furthermore, the system can use theinformation from the autoresponder generated electronic activity, forexample, the other persons, to determine an organizational structurewithin the company. For instance, if multiple autoresponder generatedelectronic activities generated responsive to multiple email addressesof different people identify the same person to contact in theirabsence, the system may determine that each of the different peoplereport to the same person or are assisted by the same person.

In another example, the electronic activity can be a bounce backelectronic activity indicating that the email address is no longeractive or the person is no longer with company. Such an electronicactivity can be referred to as a soft bounce. In such an example, thesystem can be configured to determine that the person associated withthe email address is no longer at the company by parsing the contents ofthe electronic activity. In another related example, the electronicactivity can be a bounce back electronic activity indicating that theemail was not deliverable. In such examples, the system can bedetermined to apply heuristics to determine a cause for the bounce backby identifying the email that triggered the bounce back activity. Ifthere is no other reason, such as the email size being too big, or ifmultiple recipients, connected to the system 200 have received similarnon-deliverable reports over a period of time, the system can make anassumption that the person has left the company. The system may wait formultiple bounce back electronic activities generated responsive to theemail address to confirm that the person has left the company. Upon thesystem confirming that the person has left the company via naturallanguage processing of a soft-bounce electronic activity or multiplebounce back electronic activities responsive to the same email address,the system can update the node profile to indicate that the user is nolonger at the company.

The system can further be configured to identify if any electronicactivities generated after the date that the bounce back electronicactivity was generated that mention the person's name, city and state(for example, in a signature of the email) or other values of the nodeprofile can be matched to the node profile. The system can eventuallydetermine an electronic activity that matches various values of the nodeprofile and can parse the electronic activity to identify a new emailaddress of the node profile. The system can use the bounce back activityas well as the subsequent electronic activity that matched the nodeprofile to identify various events associated with the person. Forexample, the system can determine that the person left his previous jobbefore the time of the bounce back activity and started a new job on orbefore the date of the subsequent electronic activity. In someembodiments, multiple electronic activities need to be processed toconfirm if a person has left a company or started at a new company.Furthermore, the system can be configured to update the node profile ofthe person by adding additional electronic activities including the newemail of the person once the system determines that the new emailaddress belongs to the node profile of the person. This information canbe used to generate or maintain a job timeline (e.g., start date and enddate) of the person and can be used to detect when a user changes jobsfor instance, or other information associated with the member node.

In some embodiments, auto-responder electronic activities generatedresponsive to receiving an email can include additional information thatcan be parsed to better understand the role of the person to whom theelectronic activity was sent including identifying people to contact inthe person's absence, when the person will become available if at all,and whether the person is still at the company or not.

For instance, an auto-responder generated electronic activity indicatingthat a user is on maternity or paternity leave may not include anexpected date of return or may identify one or more other people tocontact during the person's leave. The system may be configured todetect a maternity or paternity leave related autoresponder generatedelectronic activity. The system can detect the first time theauto-responder generated electronic activity was generated by analyzingmultiple electronic activities matched to the node profile of theperson. The system can then determine, based on typical maternity orpaternity leaves for the company (by monitoring other people's emailactivity in similar cases) a likely return date for the person and canupdate the node profile of the person to reflect that they are onmaternity or paternity leave. In other embodiments, the system 200 candetermine the return date by parsing the contents of the auto-respondergenerated electronic activity.

The system 200 as described herein can be configured to parse bounceback and auto-responder generated electronic activities to update nodeprofiles or determine additional information about node profiles. Insome embodiments, the system 200 can be configured to establishconnections with one or more third-party data sources, for instance,marketing automation or mass mailing systems, to receive additional datafrom such data sources. In some embodiments, the system 200 can accessthe data for companies that also provided access to their electroniccommunication servers and systems of record. The system can then harvestthe data related to bounce back activity based on electronic activitiessent via or generated by the third-party data sources, such as marketingautomation systems, and use the data related to bounce back activity toincrease the number of bounce back electronic activities the system 200ingests or can access, thereby further increasing volume of data andfurther enriching member and group node profiles and the node graph.

In addition to the examples provided herein, the system can beconfigured to provide job timeline verification, based on electronicactivities. The node graph generation system 200 (e.g., via 200electronic activity parser 210) can identify a sender and recipient ofan electronic activity. As described above, the system 200 is configuredto attempt to match the electronic activity to a node profilecorresponding to the sender and one or more node profiles correspondingto the respective recipients. In some embodiments, the system is unableto match the electronic activity to a node profile of a sender or arecipient if the system has not previously generated a node profile forthe sender or recipient. When the node graph generation system 200detects a new recipient or sender of the electronic activity, based onan identifier such as an email address, the node graph generation system200 can create a new member node profile for the new recipient orsender. The node graph generation system 200 can, in some cases,determine, using a de-duplication and identity resolution process, thatthe new member node profile matches or is the same as a previouslygenerated node profile. The node graph generation system 200 canidentify, using one or more parsing or processing techniques, a firstname and a last name associated with electronic activity. For example,the node graph generation system 200 can parse an electronic signaturein the body of the electronic activity or email to identify a firstname, last name, job title, phone number, or other contact oridentifying information. The node graph generation system 200 canidentify fields that have values that do not change when a person movesfrom one job to another, such as their first name, last name, personalphone number, or other usernames or identifiers not tied to the job. Byidentifying information that does not change with the job andinformation that likely changes with the job (e.g., company emailaddress, work phone number or job title), the node graph generationsystem 200 can map, match, or link the newly created member node profilewith a previously generated or created node profile. The previouslygenerated node profile may have included a different email address, suchas an email address with a different domain that may correspond to aprevious employer, while for example, the mobile phone number stayed thesame. The system can determine, thereafter, that the new electronicactivity associated with the new email address corresponds to the samemember node, but that the member node has switched or changed jobs.Accordingly, the system can set or establish an approximate start datein the job timeline responsive to detecting the new email address.

Further, the node graph generation system 200 can establish, set orupdate the previous job timeline with an end date. The node graphgeneration system 200 can establish, set or update the previous jobtimeline with the end date responsive to detecting bounce-back emails tothe previous email address or the last communication with the membernode profile that went through using their old email address. The nodegraph generation system 200 can further corroborate the end date basedon detecting the start date for the new job based on the new emailaddress having the new domain different from the previous domain.

The node graph generation system 200 can update additional informationabout the member node profile, such as a new company name, a new companyaddress, a new company phone number, a new email address, a new jobtitle, among others. The node graph generation system 200 (e.g., vianode profile manager 220) can detect various pieces of information withwhich to update the node profile by parsing an electronic signatureembedded or included in an electronic activity such as an email sentfrom the new email address. The node graph generation system 200 can usea statistics-driven analysis technique to determine the new companyname, the new company address, the new company phone number, the new jobtitle, among others. For example, if the sender of the electronicactivity sends 10 electronic messages to 10 different recipients withina predetermined time interval and using the same electronic signaturecontaining the same company name, company address, company phone number,and job title, then the node graph generation system 200 can beconfigured to update the node profile of the member node to reflect newvalues for company name, company address, company phone number, and jobtitle.

Furthermore, the confidence score of each of these values can bedetermined and increased as additional emails are sent and received viathe new company email address. In some cases, the confidence score inthe job title can be further determined based on the recipients of theelectronic activities. The system can be configured to maintain, for agiven job title of a person, a mapping of volume or distribution ofemails to people having certain titles. For instance, a CEO is morelikely to send emails to other CEOs or C level executives than a personhaving a title of associate. Similarly, a person with a sales relatedtitle is likely to send more outbound emails than a person with a titlerelated to Human Resources. As such, the system can be configured todetermine a confidence score of the job title based on the contributionscores of data points supporting the value but also based on whether theperson's emailing activity matches that of other people with similartitles. Stated in another way, in this example, the node graphgeneration system 200 can perform job title verification based onevaluating node profiles linked to electronic activities that identifythe person's new email address.

The node graph generation system 200 can use the member node profile tomaintain an accurate organization chart for a given company. Forexample, a field in a member node profile can include a “Reports to”field. The node graph generation system 200 can maintain, for each valueof the “Reports to” field of a node profile, an array of data pointsidentifying sources that include record objects having the “Reports to”field for the node profile to determine the confidence score of thevalue. In some embodiments, based on the values of the reports to fieldof multiple node profiles belonging to the same group node or company,the node graph generation system can maintain an organization chart forthe company. In some embodiments, job titles of various node profilescan further be used to determine the organization chart. Furthermore,the organization chart can further be determined based on parsingelectronic activities, including but not limited to out of office andother autogenerated electronic activities that may include informationidentifying links between certain node profiles. Moreover, using effortestimation and analyzing the content of electronic activities exchangedbetween two nodes, the system can further determine a relationshipbetween the two nodes including predicting a boss-subordinaterelationship between nodes.

In some embodiments, the node graph generation system 200 can detect jobtitle changes and use the detected change to reevaluate or update anorganization chart. The node graph generation system 200 can utilizemaster data model to match member nodes in a member node graph to agroup node in a group node graph (e.g., a company graph). The node graphgeneration system 200 can use the member node graph to build, generateor update a group node graph that can include a hierarchy ororganizational structure comprised of member nodes from the member nodegraph. As the node graph generation system 200 detects changes orupdates to the member node profile of a member node based on parsingelectronic activities and email signatures therein, and determines thata confidence score of a value of a field in the member node profileassociated with the detected change warrants updating the member nodeprofile, the node graph generation system 200 can update the value inthe corresponding field in the member node profile, as well as update ahierarchical organization or structure in the corresponding group nodegraph or network.

The node graph generation system 200 can present one or more member nodeprofiles for display. The node graph generation system 200 can presentthe member node profiles for display via a webpage, website, browser,application, or via other presentation medium. For example, the nodegraph generation system 200 can present a member node profile fordisplay via a mobile application executing on a client computing devicehaving a display device. In some cases, the node graph generation system200 can present the member node profile via audio output, such as via avoice interface.

The node graph generation system 200 can be configured to hide orotherwise prevent or block from display one or more fields in the membernode profile. The member node, such as the owner of the member nodeprofile, can establish the configuration as to which fields, or valuesthereof, to hide from display. The node graph generation system 200 canprovide access control options via a computing device to a member nodeor user thereof. The node graph generation system 200 can generate agraphical user interface or other type of user interface to present theaccess control options, as well as receive selections or modificationsto such access control options. Using the access control interfacegenerated and provided by the node graph generation system 200, the usercan control which fields are presented for display via the web page, forexample. In some cases, the user can control which accounts can accessthe member node profile of the user, or, on a more granular level,control which account can access which fields or values in the membernode profile.

In some cases, the node graph generation system 200 can allow athird-party device to request access or request presentation of a valueof a particular field in a member node profile. The node graphgeneration system 200 can receive the request and forward the request tothe member node via an electronic activity. The member node can acceptor reject the request. In the event the member node accepts the requestfor access to the value in the field, the node graph generation system200 can, automatically and responsive to accepting the request, updatethe access configuration profile for the member node profile. Thus, thenode graph generation system can hide or unhide one or more fields (orvalues) from one or more third-parties or computing devices based on thepreferences of the owner of the member node profile.

10. Systems and Methods for Monitoring Performance of Node Profiles

As described herein, the node graph generation system 200 can beconfigured to ingest and process large amounts of electronic activitythat are provided by one or more electronic communications serversstoring electronic activities belonging to or associated with one ormore enterprises or companies. The system 200 can only ingest andprocess those electronic activities to which the enterprises orcompanies provide access. The system 200 can also be configured toingest and process data from systems of record maintained by one or moreservers. Similar to electronic activities, the system 200 can onlyingest and process those systems of record to which the enterprises orcompanies provide access. As described herein, the system 200 canprocess electronic activities and record objects of systems of record toupdate node profiles of nodes, link or match electronic activities torecord objects of the one or more systems of record accessible to thesystem 200, determine or predict a stage of a business process, amongothers.

The node graph generation system 200 can further be configured toprocess electronic activities and record objects of one or more systemsof record of a company to determine insights for the company. Forinstance, the node graph generation system 200 can provide insights toCompany A by processing electronic activities and record objects thatCompany A has made accessible to the node graph generation system 200.The insights can include metrics at a company level, a department level,a group level, a user level, among others. The insights can identifypatterns, behaviors, trends, metrics including performance relatedmetrics at a company level, a department level, a group level, a userlevel, among others. Additional details relating to the insights aredescribed herein.

The node graph generation system 200 can include a performance module280 that can be configured to generate performance profiles for acompany. In some embodiments, the performance profile can be aperformance profile of an employee of the company. In some embodiments,the performance profile can be a performance profile of a department ofthe company, a group within a department, or individual employees of thecompany. The performance module 280 can generate the performanceprofiles using data accessible by the node graph generation system 200.In some embodiments, the performance module 280 can generate theperformance profiles using all data including electronic activities andsystems of record accessible by the node graph generation system 200from multiple companies. In some other embodiments, the performancemodule 280 can generate the performance profiles for a company onlyusing data provided by the company to the node graph generation system200. In some embodiments, the performance module 280 can be configuredto generate certain types of performance profiles for employees, groups,departments of a company that has provided access to the system 200while generating other types of reports or insights for other nodeprofiles of the system 200 that are not employees of the company.

The performance module 280 can be configured to predict employee successat a company or in a job role. The performance module 280 can, based onan analysis of electronic activities as well as information stored inone or more systems of record, predict the success of the member node.For example, the performance module 280 can generate a performanceprofile for the member node. The performance profile can be a statisticsdriven performance profile. The performance profile can be based onelectronic activities and information stored in one or more systems ofrecord. For example, the performance profile can be based on a number oramount of electronic activities associated with the member node during atime interval, a type of the electronic activities, the amount of timethe member node spends generating or preparing the electronic activities(e.g., amount of time spent writing an email), the recipients of theemail, natural language processing of the email, etc.

For example, the node graph generation system 200 (via performancemodule 280), using job history and performance history reconstructedfrom an internal member node graph, can generate a performance score,purchasing preference, decision making power, interests or otherinformation for the member node. By syncing information associated withthe systems of record and electronic activities with the member nodegraph, the node graph generation system 200 can generate or extrapolatetypes of opportunities or features on the public profile.

For example, the node graph generation system 200 can determine that amember node performs medical device sales, the member node's territoryis the northeast region, the member node prefers or is more successfulwhen doing in-person sales, the member node prefers or more successfulwhen doing CEO level sales, or an average deal size or amount. To do so,the node graph generation system 200 can parse or featurize informationcorresponding to tasks or activities (e.g., deals) associated with themember node (e.g., a salesperson or other knowledge worker) that isderived from one or more record objects stored in the one or moresystems of record. By parsing or generating features from the recordobjects, the node graph generation system 200 can update a member nodeprofile to reflect various performance information derived from recordobjects in one or more systems of record as well from electronicactivities. The node graph generation system 200 can generate variousoutputs derived from record objects in one or more systems of record andelectronic activities. Outputs can include a performance score orperformance grade indicating how well a member node has performed or mayperform in general, at a type of task, in a specific job or undercertain circumstances of a job or job environment, as determined by thecommunications metadata, extracted from the node graph.

For example, the node graph generation system 200 can generate an outputcorresponding to a performance score or performance grade of a userbased on an average seniority of attendees to a meeting initiated,established, conducted or led by the user. The node graph generationsystem 200 can determine the average seniority of attendees to themeeting established by the user by parsing electronic activitiesassociated with the meeting (e.g., calendar invite or emails) toidentify the attendees, and further determining the seniority of theattendees based on a member node profile for the attendees or metadataassociated with the electronic activities. The node graph generationsystem 200 can generate an absolute performance score based on thedetermined seniority of the attendees. In some cases, the node graphgeneration system 200 can compare the average seniority of attendees toa meeting established by a first user with the average seniority ofattendees to meetings established by other users. The system 200 can beconfigured to determine or measure the number of communications a useris involved in, the types of communications the user is having (by usingNLP and other semantic analysis techniques to determine context ofcommunications), and the roles of the people the user communicates with.These metrics or other metrics can be representative of future success.For instance, the system has been configured to determine that employeeswho drive meetings with a higher average seniority of attendees are morelikely to be successful than employees who drive meetings with a loweraverage seniority of attendees. As such, the use of the tagging engine265 and the node profiles to assign tags to meetings indicating roles ofattendees, seniority of attendees (such as CxO) can be used by thesystem to predict or measure employee performance and success.

In some embodiments, the system can be configured to track employeeactivity behavior. The system can utilize supervised or unsupervisedmachine learning to determine behaviors that result in future successfor the employee. For instance, the system 200 can determine that thenumber of communications a user is involved in, the types ofcommunications the user is having (by using NLP and other semanticanalysis techniques to determine context of communications), and theroles of the people the user communicates with are all behaviors,traits, features, metrics or other signals that can be used to predictfuture success based on training the system 200 on past or other currentemployees identified as being successful and other employees identifiedas unsuccessful.

In some embodiments, the system 200 can generate or maintain, for one ormore roles of a company, a standardized performance profile generatedbased on aggregating performance profiles of a plurality of performanceprofiles of users in the role previously identified as being successfulin the role. The system 200 can compare the users performance profilegenerated based on the users activity behavior to the standardizedperformance profile to predict a likelihood of success of the user andcan further be configured to provide feedback to the user on how toimprove their performance based on the comparison.

The system 200 can be configured to generate a performance profile of auser based on the users role as different roles may perform vastlydifferent functions. Two employees in different roles may both be verysuccessful in their roles but their electronic activity footprints mayappear very different. For instance, a successful customer successmanager's electronic activity footprint or behavior may have a regularcadence of meetings (in-person or telephonic) with each of theircustomers. Different customers may require different cadence of meetingsbut a successful customer success manager may maintain the cadence foreach of their customers. For the system to determine how well anemployee is performing, the system can be configured to monitor, foreach customer, whether the employee is having regular, recurringmeetings with the customer that matches the cadence of meetings theemployee is supposed to have with the customer. The system can determinethis based on analyzing the employee's electronic activities to see ifmeeting requests are sent within particular time periods and meetingsactually occur. As described herein with respect to tagging, the system200 can confirm whether a meeting happened and this information can beused to determine if the employee is having regular meetings. As such,the node graph generation system 200 can determine a performance of auser based on a cadence of meetings with each of the user's customers.The regularity of the cadence can be based on the number of meetingswith customers within a time interval, such as a week, two weeks, month,two months, etc.

Furthermore, the system 200 can be trained or configured to use thecadence of meetings for the user's customers to determine a user's levelof engagement with the customers. The user's level of engagement can beused as a signal to quantify a user's performance as an employee as alow level of engagement can predict that the customer may disengage withthe company or may look elsewhere to service their needs. The system 200can maintain a user engagement model for each customer or customer typethat is based on one or more parameters or metrics. The user engagementmodel can be used as a benchmark. The system 200 can then compare theuser's level of engagement with the user engagement model to determineif the user's level of engagement is below, the same or above thebenchmark. If the user's level of engagement is below the benchmark, thesystem can notify the user or the company and provide tips to increasethe level of engagement to improve customer satisfaction and/or reducethe likelihood that the customer may leave.

In some embodiments, a users performance can be measured on hiselectronic activity behavior. For instance, for employees in certainroles, the employee's performance can be based on how quickly theemployee responds to emails, how much time the employee spends preparingresponses to the emails, as well as various other metrics, parameters orattributes that can be determined from the emails the employee sends. Insome embodiments, an employee's response time to emails from a customercan be used as a metric to determine the employee's level of engagementwith the customer. The employee's response time to emails from thecustomer can be compared to the employee's response time to othercustomers to determine the employee's level of engagement with thatcustomer. Furthermore, the quality of the employee's responses may alsoprovide an indication of the employee's level of engagement. Forinstance, the system 200 can be configured to determine an amount oftime the employee spent drafting the email based on a time estimationmodel that analyzes the number of words, the choice of words, the timedifference between when the email to which the employee is responding towas received and the time the response was sent, among others. In someembodiments, the time estimation model can take into account the titlesof the participants of the email.

In another example, the node graph generation system 200 can determinethe performance score for a user based on the amount of time it takes toreceive a response to a ping or electronic activity transmitted by theuser. For example, if the user is a recruiter, the recruiter'sperformance can be based on how quickly he gets job candidates torespond to their email as well as how many job candidates respond totheir email, and how many emails (or follow up emails) on average ittakes a job candidate to respond to the recruiter. The system 200 can beconfigured to determine that recruiters that have lower response times(time it takes the candidate to reply to the recruiter) have a higherperformance score than recruiters with higher response times.Furthermore, the system 200 can be configured to determine thatrecruiters with higher response rates (number of candidates who actuallyrespond) and lower average number of emails it takes to receive aninitial response to the email perform better than recruiters with lowerresponse rates and higher average number of emails to receive initialresponses. It should be appreciated that the system 200 can beconfigured to generate such statistics for every user type or nodehaving a certain title and comparing the statistics of such users ornodes to generate a benchmark for various parameters that may be factorsthat contribute to a performance of a user or node.

In yet another illustrative example, the node graph generation system200 can determine, from the member node graph profile and one or morerecord objects in one or more systems of record, that a member nodeperforms deals in the northeast region and that the deals are morelikely to close when there a certain number of in-person meetingsassociated with that deal. The node graph generation system 200,utilizing this performance information, can generate an extrapolationcurve to determine how well the member node might perform in the future,or forecast performance of the member node. The node graph generationsystem 200 can generate the performance forecast based on historicalelectronic activities, one or more record objects, and a member nodeprofile. Similarly, the node graph generation system 200 can determinebased on a low performance score that the employee is likely to fail orleave the company.

In some cases, the node graph generation system 200 can match a membernode with a candidate deal or potential deal or ongoing deal. Forexample, the node graph generation system 200 can match a representativeto the right potential or ongoing deal. The node graph generation system200 can match the representative to the deal based on a social proximityterritory assignment, which can be based on a strength of overallrelationships of the representative with a certain type of person ormember node (e.g., buyers, such as someone who as the authority to closea deal, at target accounts). The node graph generation system 200 candetermine that the more people the representative has a relationshipwith at a target account, the more likely the representative is tosucceed with the target account. As such, the node graph generationsystem 200 can match the representative with target accounts with whichthe representative has the most relationships as well as the most ofstrong relationships with people at the target account that areassociated with closing a deal or other successful outcome.

The node graph generation system 200 can match representatives to atarget account or deal based on a selling style of the representative.For example, the node graph generation system 200 can determine that atarget account or buyer prefers a certain type of selling style, such asprimarily face-to-face vs over the phone, or meeting certain people atthe target account such as the CEO. The node graph generation system 200can then identify representatives that sell using these styles or areknown to perform well using these styles, and then assign therepresentative to the corresponding target account. For example, if arepresentative is determined to perform well when meeting a CEO based onanalyzing historical deals, electronic activities or profileinformation, then the node graph generation system 200 can match therepresentative with a target account that is associated with successfuloutcomes when the CEO of the target account meets with therepresentative.

11. Systems and Methods for Providing a Company Cloud

At least one aspect of the present disclosure is directed to systems andmethods for providing a company cloud. The company cloud can identify aplurality of companies or enterprises. Each company included in thecompany cloud can be represented as a company or group node and eachgroup node can include or be linked to one or member node profilescorresponding to people belonging to or affiliated with the company. Thecompany cloud can refer to or include a group node graph or network ofgroup nodes. A group node can be a representation of a company andinclude fields. Fields can include, for example, a company name, acompany phone number, a company address, a unique identifier for thecompany, a company size, a company location, or other informationassociated with the company. The group node can further be linked to oneor more member node profiles corresponding to people who are eitheremployed by the company or in some embodiments, have some affiliationwith the company.

In some embodiments, one or more values of the fields of the group nodecan be populated based on values of one or more member node profilesbelonging to nodes that are employed or affiliated with the company. Forexample, values of various fields such as company name, phone number,address, among others may be derived from node profiles of itsemployees. In some embodiments, the values of the fields may beassociated with value data structures including entries identifying datapoints that support the value. Such data points can be data points thatsupport values of fields of member node profiles belonging to employeesof the company associated with the group node.

Similar to how member node profiles are generated and updated asdescribed above, the node graph generation system 200 can generatecompany or group nodes using the same sources of data, namely,electronic activities and data from systems of record.

In some embodiments, the node graph generation system 200 can analyzesystems of record of different data source providers (for example,enterprises) to identify multiple account record objects representingthe same company. The multiple account record objects representing thesame company can be maintained in different systems of record belongingto different enterprises. For instance, multiple companies can maintainan account record object for the company, Acme. A first enterprise canmaintain a first account record object for the company Acme. The firstaccount record can include a first value for the field Company PhoneNumber. A second enterprise can maintain a second account record objectfor the same company Acme. The second account record object can includea second value for the field Company Address. The node graph generationsystem 200 or the data source provider network generator 260 can createa group node profile for the company Acme by extracting values from boththe first account record object and the second account record objectsuch that the group node profile is richer in information than each ofthe respective first and second account record objects. In someembodiments, the node graph generation system can further be configuredto maintain a master account record object for the company that includesvalues from each of the account record objects across multiple systemsof record such that the master account record object of the system 200is richer in information than each of the respective account recordobjects across the multiple systems of record. In the example above, thegroup node profile and the master account record object can include thefirst value for the field Company Phone Number and the second value forthe field Company Address.

The node graph generation system 200 can add or update values of one ormore fields of the group node profile for the first group node. In someembodiments, the first time the node graph generation system 200 detectsor identifies an account record object or electronic activity to beassociated with a particular group node profile of a company, the nodegraph generation system 200 can create or establish a group node profilefor the company. Thereafter, the node graph generation system 200 cancontinually amend or update that group node profile with additionalinformation or updated information. Further, as the node graphgeneration system 200 receives conflicting information for the groupnode profile from different record objects of different systems ofrecord maintained by different data source providers, the node graphgeneration system 200 can resolve the conflicts using rules, policies,or a confidence score of a value of an attribute or field of a groupnode profile, for example.

In some cases, the node graph generation system 200 can determine thatdifferent systems of record may have different values for the same fieldfor the same group node profile corresponding to a particular account orcompany. The node graph generation system 200 can use one or moretechniques to determine the correct value for the field for the groupnode profile, or the most accurate or likely to be correct value for thegroup node profile. The node graph generation system 200 can usetechniques for generating or determining the confidence score of a valueof an attribute or field in a group node profile. For example, the nodegraph generation system 200 via the attribute value confidence scorer235 can determine a confidence score for each value for each attributeor field in the group node profile based on an array of data pointsmaintained for each value.

By analyzing, parsing or otherwise processing multiple systems of recordand electronic activities, the node graph generation system 200 cangenerate a master group node profile for a company or account thatcontains one or multiple values for one or more of the fields. Similarto member node profiles, the system 200 can be configured to generate aconfidence score for each value of the one or more fields that is basedon contribution scores of each of the data points supporting the valueas evidence.

As described above with respect to member node profiles, the group nodeprofiles can also be updated as more information is ingested by thesystem 200. In some embodiments, the system 200 can ingest newelectronic activities and data from systems of record and periodicallyupdate the member node profiles based on the new data. In someembodiments, the system is configured to ingest and process new dataonce a day, once a week, among others. In some embodiments, the systemcan ingest and process new data as new systems of record are madeaccessible to the system. In some embodiments, the system can beconfigured to ingest and process new data responsive to a request from auser or an administrator of the system 200. In some embodiments, thesystem 200 can be configured to update the node graph, which can includeboth group nodes and member nodes, responsive to ingesting or processingthe data. The system 200 can be configured to update tags or associatedconfidence scores assigned to previously processed electronicactivities. Furthermore, the system 200 can be configured to updatevalue data structures of node profiles by removing electronic activitiespreviously assigned to a value but determined responsive to new datathat the electronic activities were previously assigned to a particularvalue or node profile based on insufficient data. In some embodiments,such changes to node profiles can be made responsive to determining thatthe tag or electronic activity is improperly assigned or classified. Itshould be appreciated that as more data is ingested by the system 200,certain classifications and tags can be misclassified or assigned butcan be corrected by the system based on the new data. As such, the nodegraph generation system 200 can update the group node profiles on aperiodic basis, based on a time interval, responsive to a request, orbased on new or updated insights or information that is derived fromelectronic activity data flowing through the system 200 as a time seriesdataset.

In an illustrative example, a field in an account level for a FirstCompany can be “Parent Account” field. The parent account field can havea value, linking to the record of “Second Company” because SecondCompany can be the parent company of the First Company. The node graphgeneration system 200 can determine that this is a field in an accountand then extrapolate that this field denominates a parent company in acomplex corporate structure when a Second Company owns a First Company,thereby resulting in the Second Company being named in the “ParentAccount” field. The node graph generation system 200 can analyze, forexample, 50 different systems of record to identify 50 different accountrecord objects that contain an account for the First Company. The nodegraph generation system 200 can then determine, for each of the 50different account record objects, the value of the parent account field.If all 50 of account record objects have the same value in the accountfield (e.g., Second Company), then the node graph generation system canestablish a group node profile for a group node in the master group nodegraph for the account for the First Company to include the value “SecondCompany” in the parent account field. The node graph generation system200 (e.g., via attribute value confidence scorer 235) can use one ormore policies, rules, weighting systems, scores or other logic to selecta value to use for the account field in the group node profile for thegroup node profile in the master group node graph. For example, the nodegraph generation system 200, via attribute value confidence scorer 235,can leverage a time-series calculation of values of this field acrossmultiple systems of record, while taking into account a confidence scoreand recency of each value of each field, where the more recent valuesare assigned a higher weight.

The node graph generation system 200 can analyze the systems of recordon a period basis or based on some other time interval and detect achange in values in fields. The node graph generation system 200 can,responsive to detecting a change in some or all of the systems ofrecord, update the group node profile. For example, the node graphgeneration system 200 can update the group node profile responsive todetecting the change in 5 of the 50 account record objects of the 50systems of record. The node graph generation system 200 (e.g., viaattribute value confidence scorer 235) can determine that while only 10%of the account record objects reflect a change in the value, that these10% of account record objects are reflecting an accurate change (e.g.,based on high trust scores of the systems or sources that produced thechange, and a recency of the field change) and, therefore, the mastergroup node graph is to be updated. The node graph generation system 200can determine, for example, that this 10% of systems of record areassociated with a relatively high trust score which may cause theattribute value confidence scorer to generate a higher confidence scorefor values of fields received from such systems of record or other scorerelative to some or all of the remaining 90% of systems of record. Thus,the node graph generation system 200 can detect a change in companyownership or subsidiary status based on a subset of systems of recordand before other systems of record are updated to reflect such ownershipor organizational change, thereby reducing latency in updatingorganizational structure across all systems of record, connected to thesystem.

12. Systems and Methods for Improving Member Node Performance Based onElectronic Activity

At least one aspect is directed to systems and methods for improvingmember node performance based on electronic activity. The node graphgeneration system 200, or one or more component thereof, can analyzeelectronic activities associated with member nodes to generate a membernode profile for a member node in a member node graph. The node graphgeneration system 200 can identify metrics for each member node profilebased on the electronic activities. The node graph generation system cancorrelate the metrics with desired performance outcomes or results,including but not limited to closed sales, recruited candidates, orrenewed contracts to identify which metrics are correlated with desiredperformance outcomes. Based on identifying the desired metrics thatresult in desired outcomes, the node graph generation system 200 can setone or more goals for member nodes, as well as help track those goals toincrease the likelihood that the member node achieves the desiredperformance outcome, thereby improving the likelihood that the membernode achieves the desired performance outcome.

In some embodiments, the node graph system 200 can include arecommendation engine 275. The node graph system 200 (via recommendationengine 275) can provide a recommendation or set a target goal for amember node. The node graph generation system 200 can, for example,provide these recommendations or target goals to one or more membernodes or one or more group nodes based on historical matching electronicactivities to desired performance outcomes. The node graph generationsystem 200 (or one or more component thereof) can match electronicactivities to desired performance outcomes stored or indicated in one ormore systems of record.

The node graph generation system 200 can include a performance moduledesigned and constructed to determine a performance metric orperformance level of a member node based on electronic activities. Togenerate a recommendation, the node graph generation system 200 (via aperformance module 280 and recommendation engine 275) can identifymember node performance as compared to a member node's past performanceor as compared to the performance of other member nodes that have asimilar role or otherwise share similar characteristics. The node graphgeneration system 200 (e.g., via a member node performance module) candetermine a performance of a member node. For example, the node graphgeneration system 200 can identify electronic activities associated withmultiple member nodes that are linked to a group node in a group nodegraph. The node graph generation system 200 can then identify a systemof record associated with the group node. The system of record caninclude account record objects, lead record objects, opportunity recordobjects, deal record objects or other types of record objects. Thesystem of record can include stages for any business process, such asopportunities with stages, recruiting of candidate with interviewstages, renewing contract with renewal stages, etc. In an illustrativeexample, an opportunity record object can include multiple sequentialstages for the opportunity, such as a first stage, second stage, thirdstage, and a fourth stage, where the first stage indicates an initialstage and the fourth stage indicates a final or completion stage for theopportunity. The node graph generation system 200 can correlateelectronic activities with the opportunity record objects as well as thestages of the opportunity record objects. The node graph generationsystem 200 can determine metrics based on electronic activities that areassociated with an opportunity advancing stages or not advancing stages.For example, the node graph generation system 200 can correlate that, onaverage: 5 emails and 1 in-person meeting occurred in a time intervalfor an opportunity before it moved from a first stage to a second stage;10 emails and 2 in-person meetings occurred during a time interval foran opportunity to move from a second stage to a third stage; 15 emailsand 3 in-person meetings occurred during a time interval for anopportunity to move from a third stage to a fourth stage; and 20 emailsand 4 in-person meetings occurred during a time interval for anopportunity to move from a third stage to a fourth or final stage. Bydetermining metrics that are correlated with advancing an opportunityfrom one stage to another based on electronic activities correlated withstages in opportunity record objects stored in a system of record, thenode graph generation system 200 (or component or module thereof) canpredict or forecast metrics that, when met, are likely to result in thedesired performance outcome. The node graph generation system 200 candetermine which metrics of electronic activities have the highestcorrelation to successful outcomes in order to generate goals.

For example, when a member node enters a first stage of a processdescribed in a system of record (e.g., a first stage of an opportunity,recruiting process, contract renewal, etc.), the node graph generationsystem 200 can identify, for a similar opportunity and a similar membernode, the metrics that, on average, likely resulted in a desiredperformance outcome of advancing from the first stage to a second stage.The node graph generation system 200 can further provide an indicationof these metrics to the member node as a goal or target metrics toimprove the likelihood that the member node advances from the firststage to the second stage. The node graph generation system 200 canfurther provide metrics estimated to advance from each stage to thefinal stage. The node graph generation system 200 can generate theestimate by benchmarking across member nodes in similar roles working onsimilar processes in order to identify the desired performance outcomesand metrics associated with such desired performance outcome. Forexample, the benchmarking process can include identifying member nodesthat conduct interviews in a recruiting process to identify metricsassociated with candidates that accepted an offer to join a company inorder to provide an estimate of a metric that might result in a desiredoutcome. An example metric for this example can include a response timeor response quality associated with emails between the interviewer andthe candidate before or after the interview. Other example metrics caninclude the duration of the interview, whether the interview wasface-to-face or telephonic, or whether the interviewer or candidate waslate to the scheduled interview based on natural language processing ofthe correspondence between the candidate and the interviewer.

In another example, the node graph generation system 200 can identifymember nodes linked to a group node that perform well or have desiredperformance outcomes. The node graph generation system 200 (e.g., viarecommendation engine 275) can identify a temporal aspect to the metricsassociated with the member node. The node graph generation system 200can determine when member node first joined the group node or was firstlinked to the group node (e.g., a job start date or beginning date), andhow the member node's performance and behavior metrics evolved overtime. This initial time interval can be referred to as a ramp-up period(e.g., when an employee first joins a company and then gets up to speedor ramps up). The node graph generation system 200 can identify metricsassociated with a successful ramp-up period based on identifying membernodes that are associated with desired performance outcomes based onreaching desired stages in an opportunity record object (i.e. byanalyzing how successful employees had ramped in the past). Thus, byanalyzing electronic activities and a corresponding system of record todetermine data driven metrics associated with desired performanceoutcomes determined by linking activities with record objects describingprocess stages (e.g., an opportunity record) in the system of record,the node graph generation system 200 can generate or identify goals toset for member nodes that are in a ramp-up period or other timeinterval, such as during a performance improvement plan (a plan, set upby employee's manager to bring the employee to optimal performance aftera period of poor performance). The node graph generation system 200 canfurther reevaluate the member node's metrics to update the goals or setnew goals by comparing current metrics (e.g., actual actions orperformance) associated with the member node's current electronicactivities with the desired metrics (e.g., planned actions orperformance) for electronic activities correlated with the desiredperformance outcome or result.

In some embodiments, the system 200 can be configured to compareperformances of employees of a company by monitoring the employee'scontribution to opportunity record objects and the progression of thestages the opportunity record object goes through. For instance, a highperforming employee may be involved in electronic activities that arelinked to opportunity record objects that advance from one of the stagesto another stage much quicker than another employee with the same role.Similarly, a high performing employee may be involved in electronicactivities that are linked to a greater number of opportunity recordobjects that advance from one of the stages to another stage thananother employee with the same role. as such, by tracking theopportunity record objects with which an employee is linked, aperformance of the employee can be determined and the employee's metricscan be used to set certain benchmarks that can then be used to determinea performance of another employee with a similar role or generate a rampup schedule based on the employee's metrics. For example, the node graphgeneration system 200 can determine that when a member node completes 25calls in a week, reaches out to 10 companies in a week, has 5 in-personmeetings in a week, and then writes 100 emails in the same week, thenthe member node should be able to complete a number of deals or advancea desired number of stages in one or more deals or otherwise achieve anexpected performance outcome after a certain time (e.g., a time delaybetween input activities and outcome results). The metric can refer toor include an attribute of an activity, such as an amount of theactivity. The metric can be a binary value that indicates a yes or no,such as “did you have a meeting with 10 people”, with a value of 1 or 0indicated yes or no, respectively. In some cases, the metric can be acount, a ratio, a time value, or a percentage value, based on anycombination/formula, calculated from any number of data points in themember node graph or system of records. The metrics can vary ingranularity based on the data the node graph generation system 200 cananalyze via electronic activities or one or more systems of record.Based on previous or historical activity, the node graph generationsystem 200 can predict, forecast or estimate what activity should occurto achieve a desired outcome, and propose or set goals for a member nodeor group node accordingly. The node graph generation system 200 (e.g.,via the electronic activity linking engine) can correlate the electronicactivities with the stages or desired outcomes as stored or determinedin the system of record or an opportunity record object thereof. Theelectronic activity linking engine can match, correlate, link orotherwise associate electronic activities with outcomes (e.g., advancingstages, won, lost, etc.) stored in the system of record.

The node graph generation system 200 can generate an automated employeeramp-up schedule based on the previously identified high performingmember nodes based on internal user data. The node graph generationsystem 200 identifies high performing member nodes based on electronicactivities associated with the member nodes matching desired outcomes asindicated in opportunity record objects stored in a system of record(e.g., system of record 9360) or stored in a shadow or temporary systemof record associated with the node graph generation system 200, orotherwise stored in a master system of record. With this automaticallygenerated ramp-up schedule containing metrics for electronic activitiesthat is correlated with high performing member nodes, the node graphgeneration system 200 can provide goals or recommendations to new membernodes that are beginning a new job or new role at a company. Suchrecommendations can be especially relevant for employees in sales,customer success, recruiting, or other functions.

To generate the ramp-up schedule for a new member node (e.g., a newhire), the node graph generation system 200 can identify a highperforming member node that has a node profile that is similar to themember node profile of the new member node. The node graph generationsystem 200 can compare member node profiles based on values of fields ofthe member node profiles, such as geographic area, type of industry,experience, or any other field of the member node profile. The nodegraph generation system 200 can then identify metrics associated withthe similar member node profile of the high performing member node andgenerate a ramp-up schedule using the metrics.

To identify the metrics, the node graph generation system 200 cannormalize the metrics for a time interval. The node graph generationsystem 200 can identify metrics for the high performing member node thatoccurred during a time interval that is similar or relevant to the newmember node profile. For example, the node graph generation system 200can identify the first two weeks of employment by determining when thefirst email was actually sent by the employee, and then identifying themetrics for electronic activities that correspond to the first two weeksof the high performing member node's employing at the company. Thesefirst two weeks may not indicate a high performance. For example, thehigh performing member node may not have been high performing withreference to desired outcomes in matching opportunity record objects ina system of record for another 6 months; however, the metrics associatedwith electronic activities that occurred in the first two weeks or othertime interval prior to the desired performance outcomes may nonethelessbe indicative or relevant to the high performance level of the highperforming member node. Thus, the node graph generation system 200 canselect the metrics of electronic activities that occurred in the firsttwo weeks and provide those metrics as goals or target goals or targetmetrics for the new member node without setting a goal or expectationthat the member node achieve a desired opportunity stage in the initialtime interval, but, instead, with the goal that the new member node mayachieve the desired performance with references to opportunity stagesduring a later or subsequent time interval. The node graph generationsystem 200 can correlate metrics to outcomes (e.g., all metrics ofelectronic activities that correlate with positive outcome), and thencompare new employee to a previously successful employee.

The node graph generation system 200 can normalize the time interval orotherwise account for environmental factors or external factorsassociated with the time interval that can affect the metrics associatedwith electronic activities or performance outcomes. For example, thenode graph generation system can take into account a seasonal componentby detecting a reduction in electronic activities during a vacation timeinterval. The node graph generation system 200 can determine or detectthe vacation based on identifying an automatic out of office reply inoutbound electronic activities corresponding to the member node. Thenode graph generation system 200 can determine or detect the vacationbased on identifying a vacation calendar entry electronic activitycorresponding to the member node. The node graph generation system 200can identify the vacation responsive to determining that a volume ofelectronic activity or responsiveness to electronic activities during apredetermined time interval is below a threshold for the email accountof the node profile, or the hours during which emails are sent vary froma traditional time range or time zone for the member node (e.g., whetherelectronic activities or communications are clustered around businesshours). By determining that the new member node may be on vacation—orthat a high performing member node's metrics were associated with avacation—the node graph generation system 200 can remove or filter outmetrics or data during the vacation period so as not to set improper orerroneous goals that might be faulty due to a vacation time interval, orso as not to determine that the new member node is underperforming ornot meeting goals due to the new member node being on vacation.

The node graph generation system 200 (e.g., via recommendation engine275) can provide the target goal or recommendation to the member node,or a manager member node that may then propagate the target goal toemployee member nodes. A manager member node can refer or correspond toa person whose role is a manager of employees or a team of people. Themember node profile can include a field that denominates a role of themember, such as manager or employee. The member node profile can furtherinclude a field that denominates who the manager is, such as a “managedby” field. In some embodiments, the recommendation engine 275 caninclude or interface with a machine learning engine that obtainsfeedback from a manager member node and adjusts the recommendations ortarget goals accordingly. For example, the node graph generation system200 can identify manager member nodes that are linked to employee membernodes that are performing with a desired outcome based on a system ofrecord. The node graph generation system 200 can further identify thatwhen new employee member nodes are linked or join the network of themanager member node, the new employee member node ramps up in a desiredtime interval and to a desired performance level. The node graphgeneration system 200 can receive human input from a managercorresponding to a manager member node. Based on the human input, thenode graph generation system 200 can determine that the manager membernode sets goals that are effective or successful in improving theperformance of the employee member nodes. The node graph generationsystem 200 can receive, via the manager member node or one or moreemployee member nodes, the target goals and input these target goalsinto a machine learning engine or otherwise compare the input targetgoals with automatically generated target goals to tune or update thegeneration of target goals. Thus, the node graph generation system 200(or recommendation engine 275) can receive human input from highperforming managers in order to update the recommendation engine 275 andimprove the generation of recommendations or goals for member nodes.

The node graph generation system 200 can include a performance module280 designed and configured to determine a performance of a member node.The performance module 280 can identify when metrics of a member node donot meet or exceed the target goal metrics set for the member node. Thenode graph generation system 200 can recommend to the manager toestablish, responsive to detecting that the metrics for electronicactivities for a member node do not satisfy the target goals, aperformance improvement plan for the member node. The performanceimprovement plan can be based on a difference between the member nodes'current metrics and the target metrics. The performance improvement plancan be further based on identifying a similar member node to theunderperforming member node that also previously underwent a performanceimprovement plan but is high performing now. The performance improvementplan can be based on human input received from a manager member node.Thus, the node graph generation system 200 (e.g., via recommendationengine 275) can generate a customized or tailored performanceimprovement plan that is based on a similar member node whose activitylevels and goal attainment indicates that the similar member nodesuccessfully completed a performance improvement plan and is now a highperforming member node. The node graph generation system 200 cangenerate this customized or tailored performance improvement plan usinghuman input from a manager that is deemed, by the recommendation engine275, to be a high performing manager.

The node graph generation system 200 can set performance benchmarks fora member node, a plurality of member nodes (for example, a team ofmember nodes), group nodes, industry nodes representing a plurality ofgroup nodes belonging to the same industry, nodes within a geographicterritory, or any other collection or group of nodes. The node graphgeneration system 200 can establish benchmarks for performance based onanalyzing the performance of one or more groups of nodes having similarcharacteristics. The node graph generation system 200 can identifysimilar groups of nodes based on a group size (e.g., number of membernodes in the group node), revenue of the group node, industry associatedwith the group node, geographic region of the group node, or othercharacteristic. These characteristics can be set or stored or inferredfrom a group node profile associated with the group node of a group nodegraph.

The node graph generation system 200 can generate income estimates formember nodes based on performance outcomes derived from electronicactivities associated with the member node. For example, the node graphgeneration system 200 can determine how performance outcomes map toincome, for example in sales, and then estimate income based on metricsof electronic activities that match the performance outcome stored in anopportunity record object in a system of record. The node graphgeneration system 200 can perform a deal-by-deal benchmarking todetermine an income estimate. The system 200 can identify successfulhistorical deals that are similar to a target deal. The system 200 candetermine whether the types or quantities of electronic activities (orother metrics associated with electronic activities) associated with thesuccessful historical deals are similar to the electronic activitiesmetrics that are occurring in the target deal. If the system 200determines that the target deal is on track to be a successful dealbased on the electronic activities metrics for historical similar dealsthat were successful, then the system 200 can determine that the targetdeal will be successful, or more likely to close, so the representativemember node for the deal is likely to keep a commission. The node graphgeneration system 200 can provide an indication to the member node on aperiodic or other time interval with current metrics of electronicactivities and target metrics for electronic activities in order toachieve the desired income. Based on deal-by-deal benchmarking, thesystem 200 can determine how many deals of what type the member nodeneeds to close in a year to make the desired income. Based on the numberand type of deals, the system 200 can set the goal electronic activitiesmetrics for the member node that are likely to result in closing thedeals. For example, a member node may want to make $50,000 per year,then the node graph generation system 200 can notify the member nodethat they need to have 10 in-person external meetings per week, write100 emails to external contacts, and make 25 phone calls to externalcontacts (e.g., metrics for electronic activities that are wereassociated with similar deals that were successful).

The node graph generation system 200 can detect, based on analyzingelectronic activities, whether the member node is satisfying the targetgoals. If the member node is not satisfying the target goals to achievethe desired income, the node graph generation system 200 can predict thereduction in income relative to the desired income and notify the membernode of the reduction in income that may result from missing the targetgoals. The system 200 can tie current performance level to futureprojected wins (e.g., successful deals), and hence to future projectedincome.

In some embodiments, an employee's compensation may be based on theperformance of the team that the employee is managing. For such anemployee, such as a team manager, the node graph generation system 200can help establish a compensation structure for the team manager membernode that is based on the performance of his team, which is based on theindividual performance outcomes of the employee member nodes the teammanager manages. In some embodiments, the system 200 can analyzeelectronic activities (and corresponding record objects to which theelectronic activities are matched) relating to the team managed by theteam manager to determine or predict the performance of the team. Thesystem 200 can then generate specific actions that the team manager orhis team can or should take to improve the performance of the team or toachieve previously established goals. More generally, the node graphgeneration system 200 can establish goal outcomes and recommend actionsbased on analyzing electronic activities or accessing or analyzingsystems of record. The node graph generation system 200 (e.g., viaperformance module 280) can compare electronic activity metrics oraggregated activity metadata for similar processes (e.g., sales deals,recruitment process, etc.) to determine a performance outcome for themember node participating in the process. The node graph generationsystem 200 can generate such goal outcomes or recommend actions (e.g.,electronic activities) with varying granularity, for instance, hourly,daily, weekly, bi-weekly or monthly, among others. The node graphgeneration system 200 can establish a sales compensation system based onanalyzing electronic activities or accessing or analyzing systems ofrecord. Thus, the node graph generation system 200 can automate theprocess of goal setting for team management, or setting team managementon autopilot, based analyzing electronic activities or accessing oranalyzing systems of record.

The node graph generation system 200 can set a manager member node goalof having every employee member node perform a certain number and typeof electronic activities in a certain time interval. In some cases, themanager member node goal can include aggregate activity metadataassociated with electronic activities, such as response rates fromC-level executives, meeting attendance rates, or meeting reschedulerates. The node graph generation system 200 can detect that the goal wasnot met by a first employee member node, and then perform an earlywarning prediction that the first employee member node may not beramping up on time. The node graph generation system 200 can tie thismissed goal detection with an indication that the first employee membernode may not be ramping up on time. For example, out of 50 member nodesthat succeeded at a company, their metrics trended in accordance withcurve X, whereas the first employee member node's metrics trend inaccordance with curve Y, which may not intersect with curve X, thereforethe first employee member node may not be ramping up in a satisfactorymanner. Metrics can indicate a cadence, response time to emails, numberof calls, etc.

The node graph generation system 200 can identify different patterns fordifferent industries or different types of processes (e.g., sales,recruiting, etc.). The node graph generation system 200 can establishgoals for each type of deal or opportunity or industry based on thepatterns. The node graph generation system 200 can, for example,establish patterns to advance stage with a specific OCR or champion. Forexample, the node graph generation system 200 can establish metrics forelectronic activities that are tailored or customized for the specificOCR with which the seller is interacting. For example, the node graphgeneration system 200 can estimate for a specific deal to advance to anext stage, there should be a certain number of electronic activitieswith the OCR during a time interval; so, the node graph generationsystem 200 can set that as the goal for the time interval. The number ofelectronic activities can be based on or include a number of people in ameeting, average seniority of people in a meeting, or other granularindicators.

The node graph generation system 200 can generate an effort estimationmodel for each member node based on electronic activities or metricsthereof. The metrics can indicate low responsiveness, empty times oncalendar during key business hours, or other predictors that someone isnot putting in a threshold level effort. The node graph generationsystem 200 can detect a drop off in metrics as a drop off in effort. Thenode graph generation system 200 can detect a drop off or lack ofparticipation in certain types of activities as an indication of loweffort and thus predict a person being disengaged and preparing to leavethe company.

13. Systems and Methods for Assigning Employees to Business ProcessesIncluding Leads, Accounts, and Opportunities

Companies typically assign employees to certain leads or accounts in around robin fashion. As new leads or accounts are identified, a companymay assign a different sales rep to the lead or account withoutattempting to match the sales rep to the lead or account. However, noneof the assignments of sales reps to leads or accounts or opportunitieswith such accounts is data driven, automated or objective in nature.

As described herein, companies can maintain various systems of record,including a customer relationship management system, which the companycan use as a holding system for descriptions of business processes. Thesystem of record can include lead record objects identifying leads thatthe company may pursue, account record objects identifying accounts towhich the company sells one or more products or services, opportunityrecord objects identifying deals or opportunities between the companyand the account, among others.

The present disclosure describes systems and methods for automaticallyassigning employees of a company to certain business processes of thecompany using a data driven approach. Before describing specificexamples of different business processes to which employees of a companycan be assigned, it should be appreciated that the system 200 canautomatically assign employees to certain business processes by takingadvantage or utilizing other aspects of the system 200.

As described herein, the system 200 can be configured to receive andparse electronic activities, link such electronic activities to nodeprofiles of a node graph, update the node profiles based on the contentsof the electronic activities, match the electronic activities to recordobjects of one or more systems of record of companies, generate activitypatterns of node profiles including but not limited to communicationstyles, response rates, response times, communication mode preferences,among others. These insights and others can be determined by the system200 based on the electronic activities the system 200 parses.

The system 200 can be configured to automatically assign at least oneemployee of a company to one or more record objects or providerecommendations to the company (for instance, the data source provider)to assign the at least one employee to the one or more record objects.The system 200 can be configured to automatically assign or generate arecommendation to assign a business process or associated record objectto an employee of a company associated with the business process.Perhaps, more generally, the system 200 can automatically match orgenerate a recommendation to match or pair an employee of a company anda record object of a system of record of the company.

In some embodiments, to do so, the system 200 can be configured tomaintain, for each employee of the company, an availability of theemployee based on a status of one or more record objects to which theemployee is assigned. In some embodiments, the employee can be assignedto a first number of lead record objects, a second number of accountrecord objects and a third number of opportunity record objects. Thesystem 200 can further determine, for each of the opportunity recordobjects, a stage of the opportunity record object. Moreover, the systemcan determine an amount of time the employee needs to spend on theopportunity based on the stage of the opportunity record object, a sizeof the deal associated with the opportunity, an expected or predictedtime frame for closing the opportunity, and other parameters associatedwith the opportunity record object. The system can determine, based oneach record object to which the employee is assigned, an availabilityschedule of the employee that identifies the employee's availabilityduring various time periods, including for example, the next week, thenext two weeks, the next month, the next quarter, the next year, amongothers.

The system 200 can be configured to automatically match or generate arecommendation to match or pair an employee of a company and a recordobject of a system of record of the company by using one or more rulesthat may be specific for different types of record objects. In someembodiments, the rules can be learned by analyzing previous matchesbetween employees and record objects and the success or failures of suchmatches. The rules can be learned using machine learning or othertechniques.

The following sections describe how the system can automatically matchor generate a recommendation to match or pair an employee of a companyand different types of record objects of a system of record of thecompany.

A. Matching Employees and Lead Record Objects

This section relates to matching employees and lead record objects,assigning employees to lead record objects or assigning lead recordobjects to employees. A lead record object can identify a person who canbe an early interest for the company. Determining how successful anassignment of a lead to an employee is likely to be is based on severalfactors. Some of these factors include i) a quality of the lead; ii)behaviors or business practices of the employee; iii) behaviors orbusiness practices of the lead; and iv) availability of the employee toservice the lead, among others. Out of these factors, the availabilityof the employee to service the lead can be a more important factor. Thismakes sense because a salesperson currently working on 5 late stagedeals likely will not have the availability to service the lead, whichwill result in the company losing the lead because the salesperson wasunable to commit enough time to building a relationship with the lead.Examples of behaviors or business practices of the employee can be theirpreferences to want phone calls over emails or in person meetings, adesired time of day factoring in their time zone during which theemployee likes to communicate with leads, or an employee's comfort levelwith dealing with leads having certain titles, for example, CIO, CEO orother executive level leads. Similarly, the behaviors, preferences andbusiness practices of the lead can also be relevant.

In one embodiment, the system 200 can be configured to first determine,for a given lead, a plurality of employees of the company that may bepotentially be assigned to the lead. These employees may besalespersons. The system 200 can then determine the availability of eachof the salespersons and based on their respective availabilities, thesystem 200 can select a subset of the salespersons as candidatesalespersons. The system can then determine, for each candidatesalesperson, the behaviors, preferences and business practices of thecandidate salesperson that the system 200 can derive from parsingelectronic activities linked to a node profile of the candidatesalesperson. The system 200 can then compare the determined behaviors,preferences and business practices of the candidate salesperson tobehaviors, preferences and business practices of the lead (which canalso be determined by the system 200 by parsing electronic activitieslinked to a node profile of the lead). In some embodiments, the system200 can be configured to determine the behaviors, preferences andbusiness practices of the candidate salesperson as it relates to thelead by only analyzing electronic activities exchanged between thesalesperson and other leads in the past. Similarly, the system 200 canbe configured to determine the behaviors, preferences and businesspractices of the lead as it relates to the plurality of candidatesalespersons by only analyzing electronic activities exchanged betweenthe lead and other salespersons in the past. The system 200 can thendetermine a match score between the candidate salesperson and the leadbased on the comparison and either automatically assign the candidatesalesperson to the lead or vice versa or provide a recommendation toassign the candidate salesperson to the lead or vice versa to theadministrator or user of the system of record in charge for assigningleads to employees.

In some embodiments, the system 200 can use other signals or factors formatching leads to employees. For instance, if the system 200 candetermine if the lead has any prior connection with any of the candidatesalespersons and also determine a connection strength and type ofconnection between the lead and the candidate salesperson. As describedherein, the system 200 can maintain a connection strength between nodeprofiles of the system 200 and as such, the system can use theconnection strength between the lead and the candidate salespersons as afactor to determine which candidate salesperson to match to the lead.

The system 200 can be configured to assign different weights todifferent factors used for matching leads and employees. In someembodiments, the system can enable each company to establish its ownrules or policies for recommending matches between leads and employees.In some embodiments, the system 200 can be configured to train a machinelearning model to match leads and salespersons based on analyzing asalesperson's matches with leads in the past as well as analyzing thelead's matches with other salespersons in the past.

By way of this solution, the system 200 can reduce the number ofcandidate salespersons the company needs to consider for each new leadthereby allowing the person responsible for assigning leads to employeesto spend less time pairing leads to employees while improving thelikelihood of success of converting the lead by selecting candidateemployees that are most likely going to succeed with this lead based onobjectively analyzing historical electronic activities. Moreover, atpresent, companies are focusing on lead generation without optimizingthe conversion of existing leads. The solution described herein aims todetermine which employee is most likely to convert the lead to optimizethe company's ability to convert each and every lead of the company.

B. Matching Employees and Account Record Objects

In contrast to the concept of matching employees and leads describedabove, this section relates to matching employees to accounts. Anaccount or an account record object corresponds to a customer of thecompany. Each account can be linked to one or more lead record objectsand opportunity record objects. In contrast to lead assignmentsdescribed in the previous section, account assignment is similar exceptthat a lead is one person while an account includes a group of people.

The system 200 can be configured to identify an account of a company towhich to assign one or more employees of the company to service theaccount. The system 200 can be configured to identify each of thecontacts at the account. The contacts may be identified by analyzing thecontact record objects of the system of record to identify whichcontacts are linked to the account. In some embodiments, the system canutilize the node graph of the system to analyze ode profiles thatcurrently work at the account. The system can then run an analysis foreach employee of the plurality of employees of the company that may be acandidate to service the account based on the employee's function or jobdescription. Upon selecting a set of candidate employees from theplurality of employees of the company, the system 200 can determine, foreach employee, a connection strength between the employee and each ofthe contacts at the account. The system can then aggregate, for theemployee, the connection strengths between the employee and each of thecontacts by applying different weights based on the role, title orfunction of the contact within the account, which can all be determinedby the system through the system of record or the node profilesmaintained by the system 200. The system can then determine, from theaggregated connection strengths of each of the plurality of employees,at least one employee to assign or recommend assigning to the account.As mentioned above, the contacts at the account with which the employeehas relationships can be weighted based on their role, title orfunction.

In some embodiments, the system 200 can take into account other factorsother than connection strengths prior to assigning the employee to theaccount. In particular, the system 200 can also consider thegeographical proximity between the employee and the account or thecontacts within the account. The system 200 can also consider theemployee's selling style or other behavioral patterns and compare themto the buying style of the contacts within the account to determinewhether or not to assign the employee to the account. In addition, thesystem 200 can take into account past experiences of the employee withthe contacts or the account itself. For instance, the system 200 candetermine if the employee has previously worked for or with the accountat a previous job. The system 200 can also determine if the employee haspreviously worked with any of the contacts included in the account. Thesystem 200 can also determine if the employee has previously worked withsimilar types of accounts, for instance, if the account is Verizon, thesystem can determine if the employee has worked with AT&T given thatAT&T is in the same sector as Verizon and so the employee may be abetter fit for an account such as Verizon. In some embodiments, thesystem 200 an also determine an availability of the employee todetermine if the employee has the capacity to service the account.

In some embodiments, the system can determine a target persona for theaccount. For instance, if the account is a marketing department of acustomer, the system can be configured to generate a target persona thatcorresponds to the marketing department as opposed to an accountsdepartment. The system can then attempt to identify employees within thecompany that most closely match the target persona corresponding to themarketing department as this employee will be most likely to best servethe account.

The system 200 can also be configured to take into account otheremployees to assign to the account as part of a sales team. As such, thesystem 200 may be configured to determine whether or not to assign anemployee to the account based on which other employees are alreadyassigned to the account or are candidates to be assigned to the account.The system 200 can be configured to assign an employee to the accountbased on the employee's relationship with other employees who arealready assigned to the account, for instance, the employee is part of 3other sales teams that include the other employees.

In addition, the system 200 can be configured to recommend additionalemployees to assign to the account based on selecting an employee toassign to the account. For instance, the system 200 can identify a firstemployee as a sales representative to the account. The salesrepresentative generally works with a sales engineer and an accountexecutive when selling to a company. As such, the system 200 can beconfigured to select a sales engineer from a plurality of candidatesales engineers and select an account executive from a plurality ofcandidate account executives to assign to the account based ondetermining that the sales engineer and the account executive have beenincluded in sales teams with the sales representative for otheraccounts.

In some embodiments, the system 200 can be configured to recommendoverlay resources like sales engineers to the account. The salesengineer that is recommended may be selected for recommendationresponsive to the system determining that the sales engineer also hasconnections to the account. In addition, the system 200 can furtherrecommend executives on the company side to which to recommend or assignto the account. By generating these additional recommendations ofemployees to the account, the system can be configured to automaticallyrecommend or generate account team recommendations that the company canuse to build account teams. As described herein, these account teams canbe based on their relationships with contacts at the account, their pastexperiences with the account their past experiences working with eachother on other accounts, as well as their availability to service theaccount, among others.

In some embodiments, the system can identify one or more people at theaccount who may be considered to form the buying group. The buying groupcan be determined by the system 200 using the node graph of the system200 or from other systems of record accessible to the system 200. Thesystem 200 can be configured to identify employees to assign to theaccount based on the target persona of the account as well as the buyinggroup of the account. In some such embodiments, the system can adopt thesame techniques and methodology described herein but adjust weights ofcertain factors based on the target persona of the account as well asthe buying group of the account.

As described herein, the system 200 can be configured to detect accountteams from electronic activities that are matched to record objectscorresponding to account record objects or opportunity record objects.Detecting that an employee belongs to an account team based onelectronic activities can be useful to the system 200 for matching theelectronic activities identifying the employee to the appropriate recordobject of a system of record, among others. In some embodiments, anaccount team can be determined from the system of record based onlinking contact record objects to an account record object, forinstance. However, it should be appreciated that the system 200, asdescribed with respect to Section 12, is configured to providerecommendations of employees to add to existing account teams or createnew account teams for new accounts.

As described above with respect to matching electronic activities torecord objects, the system 200 can be configured to identify candidaterecord objects to match electronic activities based on account teams. Bybeing able to expand the account teams or verify if an employee shouldbe added to an account team, the system 200 can be configured to improveits ability to match electronic activities to record objects by betteridentifying record objects using matching strategies involving accountteams.

C. Matching Employees and Opportunity Record Objects

The system can be configured to also be configured to automaticallyassign or recommend assigning an employee to an opportunity recordobject. The system can identify employees to match to opportunity recordobjects in a manner similar to lead record objects and account recordobjects. As opportunities are business processes that need activeinvolvement in the short term, selecting an employee to assign to theopportunity, the system can give more importance to the employee'savailability in the short term relative to when the system selects anemployee to match to an account. The system can be configured todetermine, for each employee, their current load or available capacitybased on the number of opportunities the employee is working on, whatstage each of the opportunities is in, among others. As such, adescribed here, the system's ability to predict stage classification ofopportunity record objects can be used to determine the employee'savailability and based on the employee's availability, a recommendationto assign the employee to one or more opportunities that the employee isnot currently assigned to.

The system can be configured to identify, for a given opportunity, oneor more opportunity contact roles associated with the opportunity aswell as other contacts at the account level that are involved with theactivity. The system can then determine to identify candidate employeesthat would be a good fit for the opportunity based on a comparison ofthe candidate employee and the contacts involved or likely to beinvolved with the opportunity. Based on the determination, the systemcan provide a recommendation to add a candidate employee to the accountteam servicing the opportunity record object. As described above withrespect to matching employees and lead record objects and account recordobjects, the system can determine similar factors to determine how gooda fit the candidate employee will be for the opportunity.

D. Matching Employees and Named Account Lists

The system 200 can be configured to match employees to one or moreaccount lists. In a scenario where a new employee joins a company, asupervisor may be assigned to assign the employee to multiple accountsor leads, among others. At present, the supervisor may simply assign theemployees to accounts based on a geographical location of the employeeand corresponding locations of the accounts. However, assigningemployees to accounts simply based on location matching fails tooptimize the employee's ability to generate new leads and opportunities.

In some embodiments, the system 200 can be configured to generate a listof accounts to which to assign an employee of the company. In someembodiments, the system 200 can receive a request from a user of thesystem 200 to assign or identify accounts of the company to theemployee. The system 200 can first identify all of the accounts of thecompany to which the system 200 can possibly match the employee. Thesystem 200 can then determine, for each account, one or more contacts atthe account with which the employee has a connection. The system 200 canuse the node profiles and node graph to determine these contacts. Thesystem 200 can then determine, for each contact with which the employeehas a connection, a connection strength between the contact and theemployee. The system 200 can then weight each of these connections basedon the account and the role of the contact within the contact. Thesystem 200 can then determine an aggregated score between the accountand the employee based on the weights and connection strengths of theemployee with the contacts of the account. The system can compute theaggregated score also by factoring in a location of the employeerelative to the account, a time zone of the employee relative to theaccount, a selling style or communication style of the employee relativeto the buying style or communication system of the contacts within theaccount. The system can then generate a list of accounts to which tomatch the employee based on the aggregated scores between the employeeand the respective account. It should be appreciated that other factors,such as the employee's availability, timing of potential opportunitiesof the account, other employees that may likely form the account team,can also be factors in computing the aggregated score between theemployee and the respective account.

E. Matching Employees and Territories

Some companies may maintain one or more systems of record in whichemployees are assigned to territories, such as geographical regions. Insome such cases, the system 200 can be configured to assign employees toterritories, which may be assigned to certain accounts. Similar to howthe system 200 can determine an aggregate score for each accountdescribed above, the system 200 can be configured to assign employees toterritories based on determining an aggregate score between theterritory and the employee by determining individual scores betweenaccounts within the territory and the employee.

It should be appreciated that matching employees to various recordobjects or business processes described above can be based on objectivedata that is parsed from electronic activities involving the employee orelectronic activities involving leads or contacts at the accounts. Assuch, the system 200 can be configured to rely on certain electronicactivities when determining which record objects or business processesto match or assign or which employee to assign or match to the recordobjects or business processes of the company. In this way, a data-drivenapproach to selecting employees to assign to accounts can be achieved,which can result in better outcomes for the employee, the account, andthe company.

It should further be appreciated that similar methodology can be used bythe system 200 for identifying potential candidates to suggest to acompany to hire as employees. The system 200 can analyze a candidate'sconnections and communication style from electronic activities linked tothe node profile of the candidate and use that information to compare toaccounts of the company to determine if the employee will be a good fit.Similarly, for a person looking to join new company the system 200 canidentify potential candidate companies to the person based on theperson's connections and communication style determined by the system200 from electronic activities linked to the node profile of the personand information about the candidate companies and their respectiveaccounts also maintained by the system 200.

14. Systems and Methods for Generating Data Recommendations Based on anImmutable Member Node Network

At least one aspect of the present disclosure is directed to systems andmethods for generating data recommendations based on an immutable membernode network. The immutable member node network can refer to or includea member node network containing member nodes connected to one or moreother member nodes. The member nodes can contain a member node profilethat is generated by the node graph generation system 200 (or nodeprofile manager 220) using electronic activity information orinformation from a master system of record. By using electronic activityinformation or a master system of record generated and maintained by thenode graph generation system 200, the node graph generation system 200can generate data recommendation using the immutable member nodenetwork.

The member node network may be immutable in that the member node networkmay be accurate and not contain erroneous data, or lack data with aconfidence score that falls below a threshold. The node graph generationsystem 200, using the member node network (e.g., member node graph) canmatch member nodes to a potential group node, job, account, oropportunity based on the member node profile matching profiles, metricsor parameters associated with the group node, job, account oropportunity.

For example, the node graph generation system 200 can determine that aparticular member node is represented by a member node profile thatincludes fields and values for the fields. The node graph generationsystem 200 can further determine, via a member node performance module280, a performance score as well as performance metrics for the membernode. The performance information can be correlated with metricsassociated with electronic activities. The performance metrics can begranular and correspond to profile values. For example, the member nodeprofile performance information can indicate that a member node has ahigh performance level when the member node performs electronicactivities that include at least a first number of in-person meetingswith C-level executives. For example, if the member node has fivein-person meetings with C-level executives in a week, then theperformance module 280 can determine that the member node is performingwell based on historical performance information for the member node orsimilar deals. The node graph generation system 200 (or member nodeperformance module 280) can determine the high performance level (e.g.,relative to an average performance level across member nodes or a subsetof member nodes). The node graph generation system 200 can then identifygroup nodes or group profiles associated with group nodes or companiesthat match the profile values correlated to the high performance levelof the member node.

As described above also with respect to Section 12, the system 200 canbe configured to utilize information included in the node graph to matchcandidate employee and companies based on the candidate employee'sconnections with one or more accounts of the companies to which thesystem 200 determines a match. In some embodiments, the system 200 candetermine a match based on a candidate employee's selling style and abuying style of a buyers group of an opportunity linked to one of theaccounts of a company. It should be appreciated that the system 200 canlook at other signals too when making such matches and not rely simplyon matching according to a selling style or for a single opportunity. Insome embodiments, the system 200 can identify an employee within acompany that should be put on an account team of an opportunity recordobject based on the selling style of the employee (or other factors suchas availability, connections to the buyer group, among others).

Thus, the node graph generation system 200 can match member nodes to agroup node based on performance characteristics or other metrics of themember node and the group node that are derived, inferred, or otherwisedetermined using electronic activities from data source providers 9350and record objects from one or more systems of record. The node graphgeneration system 200 can use the electronic activities and the data inthe systems of record to generation a performance profile for a membernode, which can be stored in a master member node network or immutablemember node network. The member node network can be immutable because itis not self-written or self-reported by individuals; instead, the nodegraph generation system 200 generates the performance profile and membernode profile using electronic activities and systems of record, which isan independent, factual, and objective source of activity information.The node graph generation system 200 can generate the group node networkcontaining group profiles for group nodes. The node graph generationssystem 200 can identify granular values that are correlated with desiredperformance or outcomes based on stages of opportunities or stages ofother business processes, stored in record objects of a system of recordor one or more systems of record.

As described herein and supplemental to the description of various termsprovided above, electronic activities can include emails, electroniccalendar events, electronic meetings, phone call logs, instant messages,other any other electronic communications generated by a node, receivedby a node, exchanged between nodes or otherwise stored on an electronicserver configured to provide electronic activities to the dataprocessing system 9300.

An individual or member node can be an electronic representation of auser, person, account of a person or user, an employee, a bot, or anyother entity that may have an account or an identifier that the dataprocessing system can generate a node profile for.

A group node can be an electronic representation of an enterprise, acompany, an organization, an employer, a team of employees or people, ora plurality of member nodes that can be treated as a single entity.

A node profile can be an electronic representation of a profile of amember node or a group node. The node profile can include fields. Eachfield can include one or more values. An example field can be an emailaddress. An example value can be john.smith@example.com.

A value of a field can include an array of data points identifyingoccurrences of the value. Each value can have a confidence score.

A data point can identify an electronic activity or other piece ofinformation that contributes the value to the field. The data point caninclude or identify a source of the electronic activity, a trust scoreof the source of the data point, a time or recency of the electronicactivity and a contribution score.

The source of the electronic activity can be a mail server, a system ofrecord, or any other repository of electronic activities.

A trust score of the source of the data point can indicate atrustworthiness of the source of the data point. The trust score of thesource can be based on a completeness of system of record maintained bythe source. The trust score can also serve as an indication of howreliable the source may be.

A contribution score of the data point can indicate how much the datapoint contributes towards a confidence score of the value associatedwith the data point. The contribution score can be based on the trustscore of the source, a health score of the source, and a time at whichthe data point was generated or last updated.

A confidence score of the value can indicate a level of certainty thatthe value of the field is a current value of the field. The higher theconfidence score, the more certain the value of the field is the currentvalue. The confidence score can be based on the contribution scores ofindividual data points associated with the value. The confidence scoreof the value can also depend on the corresponding confidence scores ofother values of the field, or the contribution scores of data pointsassociated with other values of the field.

A confidence score generally relates to a level of confidence that acertain piece of information is accurate. As used herein, a confidencescore of a piece of information, such as an assigned tag, a value of afield of a node profile, a stage classification prediction, a recordobject match, can indicate a level of confidence that the piece ofinformation is accurate. The confidence score of the piece ofinformation can change based on a temporal basis. A node profile caninclude a first email address corresponding to a first job and a secondemail corresponding to a subsequent job. Each of the two email addressesare at respective points in time, accurate and valid. As the personswitches jobs, the first email address is no longer valid but theconfidence score associated with the email address can in someembodiments, remain high indicating that the first email address belongsto the node profile. Similarly, the second email address also belongs tothe node profile and therefore also has a high confidence score. Afterthe system determines that the second email address is active andfunctioning, the system can assign a higher confidence score to thesecond email address relative to the first email address since thecontribution scores provided by recent data points (for example, recentelectronic activities identifying the second email address) cancontribute towards the higher confidence score. Similarly, any tags thatare assigned to electronic activities identifying bounce back activityrelated to the first email address (indicating that the first emailaddress is no longer active) can reduce the confidence score of thefirst electronic activity.

The health score of the source can indicate a level of health of thesource. The health of the source can include a completeness of thesource (for example, a system of record), an accuracy of the dataincluded in the source, a frequency at which the data in the source isupdated, among others.

A connection strength between two nodes can be based on the electronicactivities associated with both the nodes. In some embodiments, eachelectronic activity can be used by the system to determine a connectionstrength between the two nodes. The contribution of each electronicactivity towards the connection strength can diminish over time as olderelectronic activities may indicate a past connection but do not indicatea current status of the connection strength between the two nodes.

The time decaying relevancy score of an electronic activity can indicatehow relevant the electronic activity is for determining a connectionstrength between two nodes exchanged between or otherwise associatedwith the two nodes. The connection strength between two nodes can bebased on the time decaying relevancy scores of all of the electronicactivities exchanged between or otherwise associated with the two nodes.

As further described herein, electronic activities can be linked to ormatched to record objects. Record objects can be maintained in a shadowsystem of record maintained by the system 9300 or in some embodiments,linked or matched to record objects maintained in master system ofrecords that are maintained by customers or enterprises.

15. Matching Electronic Activities Directly to Record Objects of Systemsof Record

As described above, the system described herein can match electronicactivities with one or more record objects. The system can match theelectronic activities in a single-tenant or multi-tenant configurationof the system. For example, in a single-tenant configuration, the systemcan receive or access electronic activities from a single data sourceprovider and match the electronic activities to record objects of asystem of record of the data source provider from which the electronicactivities were received or accessed. In a multi-tenant configuration,the system can receive or access electronic activities from multipledata source providers and match the electronic activities to recordobjects of a system of record of the respective data source providerfrom which the electronic activities were received or accessed. Asdescribed herein, the system can automatically match, link, or otherwiseassociate the electronic activities with one or more record objects. Foran electronic activity that is eligible or qualifies to be matched withone or more record objects, the system can identify one or more set ofrules or rule sets. Using the rule sets, the system can identifycandidate record objects. The system can then rank the identifiedcandidate record objects to select one or more record objects with whichto associate the electronic activity. The system can then store anassociation between the electronic activity and the selected one or morerecord objects.

FIG. 16 illustrates a block diagram of an example process flow 1600 forprocessing electronic activities in a single-tenant configuration. Also,with reference to FIGS. 3 and 4, among others, the data processingsystem 9300 can be in communication with one or more data sourceproviders 9350. Each of the data source providers 9350 can include adata source 9355. FIG. 16 illustrates an example of a single-tenantsystem where the electronic activities 9305 from a single tenant (e.g.,the data source provider 9350 that includes the data source 9355) ismatched to the record objects 1602 of a single shadow system of record9330. The single shadow system of record 9330 can be associated with thedata source provider 9350 that provided the electronic activity. Forexample, the shadow system or record can include data retrieved from therecord objects of the data source providers system of record. It shouldbe appreciated that although FIG. 16 illustrates a shadow system ofrecord including one or more shadow record objects that correspond torespective record objects of a corresponding system of record of thedata source provider, the data processing system 9300 is configured todirectly match the electronic activities of the data source provider tothe record objects of the system of record 9360 without having to firstmatch the electronic activity to a shadow record object of the shadowsystem of record 9330.

The data source provider 9350 can store electronic activity9305(1)-electronic activity 9305(N) (generally referred to as electronicactivity 9305) in the data source 9355. As described above, theelectronic activities can include one or more forms of electronicactivity, such as email or other forms of electronic communication. Thedata processing system 9300 can access or otherwise retrieve theelectronic activity 9305 from the data source 9355. For example, theabove-described electronic activity ingestor 205 can be configured toingest electronic activities in a real-time or near real-time basis foraccounts of one or more enterprises, organizations, companies,businesses, institutions or any other group associated with the datasource providers. The electronic activity ingestor 205 can ingestelectronic activities. For example, when a data source providersubscribes to a service provided by the data processing system 9300, thedata source provider can provide access to electronic activitiesmaintained by the data source provider by going through an onboardingprocess. That onboarding process can enable the data processing system9300 to access electronic activities owned or maintained by the datasource provider in one or more data sources 9355. For example, the datasources 9355 can be, but are not limited to, mail servers, one or moresystems of record, one or more phone services or servers of the datasource provider, among other sources of electronic activity. Theelectronic activities ingested during an onboarding process may includeelectronic activities that were generated in the past, perhaps manyyears ago, that were stored on the electronic activities' sources. Thedata processing system 9300 can be configured to ingest (and re-ingest)the electronic activities from one or more data sources 9355 on aperiodic basis, including daily, weekly, monthly, or any reasonablefrequency.

The data processing system 9300 can match the electronic activities 9305to one or more record objects 1602 of the shadow system of record 9330.The record objects 1602 of the shadow system of record 9330 can besynced with the record object 1602 of the system of record 9360. Syncingthe shadow record objects 1602 with the record objects 1602 of thesystem of record 9360 can include adding values from fields of theshadow record objects 1602 to the corresponding values, such as matchedelectronic activities 9305, of the record objects 1602 in the system ofrecord 9360. In some embodiments, the data processing system 9300 canmatch the electronic activities 9305 directly to the system of record9360. For example, the data processing system 9300 can match theelectronic activities 9305 to the record objects in the system of record9360 without matching the electronic activities 9305 to the recordobjects in the shadow system or record 9330.

FIG. 17 illustrates a block diagram of an example process flow 1700 forprocessing electronic activities in a multi-tenant configuration. Asillustrated by the process flow 1700, the multi-tenant configuration caninclude a plurality of data sources 9355(1)-9355(N), each of which canbe a component of a respective data source provider 9350(1)-9350(N). Thedata processing system 9300 can receive or access electronic activities9305 from each of the respective data sources 9355(1)-9355(N).

The data processing system 9300 can identify from which of the datasources 9355, each of the respective electronic activities 9305 werereceived and then match the electronic activities 9305 with one or morerecord objects 1602 associated with the data source provider 9350.

For example, and as illustrated in FIG. 17, the data source 9355(1) canbe associated with the shadow system or record 9330(1), the data source9355(2) can be associated with the shadow system or record 9330(2), andthe data source 9355(N) can be associated with the shadow system orrecord 9330(N). The data processing system 9300 can match the electronicactivity 9305(1), from the data source 9355(1), with two of the recordobjects 1602 in the shadow system of record 9330(1). The data processingsystem 9300 can match the electronic activity 9305(2), from the datasource 9355(2), with two of the record objects 1602 in the shadow systemof record 9330(2). The data processing system 9300 can match theelectronic activity 9305(N), from the data source 9355(N), with one ofthe record objects 1602 in the shadow system of record 9330(N).

In some embodiments, the data processing system 9300 can match theelectronic activities 9305 directly to the system of records 9360. Forexample, the data processing system 9300 can match the electronicactivities 9305 to the record objects in the system of record 9360without matching the electronic activities 9305 to the record objects inthe shadow systems or record 9330.

FIG. 18 illustrates a block diagram of an example process flow 1800 formatching electronic activities 9305 with record objects 1602. The datasource 9355 includes a plurality of electronic activities 9305 that areaccessed by or transmitted to the data processing system 9300. The dataprocessing system 9300 can include a filtering rule set 1701 andmatching rules 1702. The data processing system 9300 can use thefiltering rule set 1701 and the matching rules 1702 to map the incomingelectronic activities 9305 to one or more of the record objects 1602 inthe system of record 9360.

Also, with reference to FIGS. 11 and 12, among others, the dataprocessing system 9300 can include one or more filtering rule sets 1701.The filtering rule sets 1701 can include rule sets for filtering orexcluding electronic activities 9305 from the matching process. Forexample, when the data processing system 9300 processes an incomingelectronic activity 9305, the data processing system 9300 can firstprocess the electronic activity 9305 with the filtering rule sets 1701before attempting to match the electronic activity 9305 with a recordobject 1602. As illustrated in FIG. 18, the electronic activity 9305(1)can be received by the data processing system 9300. The data processingsystem 9300 can process the electronic activity 9305(1) with thefiltering rule set 1701 before the data processing system 9300 passesthe electronic activity 9305(1) to the matching rules 1702. Asillustrated in FIG. 18, the electronic activity 9305(1) is processedwith the filtering rule set 1701 and is restricted from furtherprocessing and is not matched with one of the record objects 1602.

The filtering rule set 1701 can include a plurality of rules orheuristics for determining whether the electronic activity 9305 shouldbe restricted from further processing including matching the electronicactivity to a record object. The rules can be keyword-based. Forexample, the rules can include a list of keywords. The data processingsystem 9300 can process the text of the electronic activity 9305 anddetermine whether one or more of the keywords are present in theelectronic activity 9305. The data processing system 9300 can determinethe electronic activity 9305 should be restricted if the data processingsystem 9300 identifies one of the keywords in the electronic activity9305. The data processing system 9300 can identify identical matches ofthe keyword. The data processing system 9300 can identify approximate orfuzzy matching of the keyword (e.g., the data processing system 9300 canidentify misspellings or plurals of the keyword). In some embodiments,the keywords can include wildcards. For example, the keyword may be onlythe base or root of a word. The rules can be pattern-based. For example,the rules can include regex patterns with which the data processingsystem 9300 processes the text of the electronic activities 9305. Forexample, the regex pattern can be configured to identify social securitynumbers.

If the data processing system 9300 determines that the electronicactivity 9305 is selected with one of the rules of the filtering ruleset 1701, the data processing system 9300 can stop further processing oringestion of the electronic activity 9305. For example, if theelectronic activity 9305 is an email that includes a social securitynumber and one of the rules of the filtering rule set 1701 is configuredto identify social security number patterns, the data processing system9300 can identify the email with the rule and stop ingestion of theemail such that the email is not matched to one of the record objects1602. The electronic activities 9305 identified by the filtering ruleset 1701 can be ingested but are restricted from being matched to one ormore record objects. For example, the electronic activity 9305 may berestricted from being matched to a record object, but the dataprocessing system 9300 can use the data in the electronic activity 9305to populate fields with values in the above-described node profilegraph.

The data processing system 9300 can include one or more matching rules1702. The rules of the matching rules 1702 can include rules formatching electronic activities 9305 with one or more record objects1602. Also referring to FIGS. 11 and 12, among others, the rules formatching the electronic activities 9305 to record objects 1602 can begrouped into sets such as buyer-side rules or strategies that matchelectronic activities 9305 to record objects 1602 based on data relatedto the recipient of the electronic activity 9305. Another examplefiltering rule set 1701 can include a grouping of rules based onseller-side rules or strategies that match electronic activities 9305 torecord objects 1602 based on data related to the sending of theelectronic activities 9305. The data processing system 9300 can matchthe electronic activities 9305 to the record objects 1602 based on aplurality of matching rules 1702. For example, the electronic activity9305(3) is matched with the record object 1602(3) based on a pluralityof matching rules 1702(1). In some embodiments, the matching rules 1702can be to select a group of record objects. The data processing system9300 can then select a candidate record object from an intersection ofthe groups of record objects. For example, the candidate record objectmay be the record object that is selected by each of the matching rules1702.

FIG. 19 illustrates a method 1900 to match electronic activitiesdirectly to record objects. The method 1900 can include accessing aplurality of electronic activities (BLOCK 1902). With reference to FIGS.16-18, among others, the data processing system 9300 can access aplurality of electronic activities. The electronic activities can betransmitted to the data processing system 9300 from data sourceproviders. The data processing system 9300 can retrieve the electronicactivities from the data source providers. For example, the data sourceprovider can include or be an email server. The data processing system9300 can have the authority to access the emails stored on the emailserver through an API or an HTTP method (e.g., a GET method).

The method 1900 can include accessing a plurality of record objects(BLOCK 1904). The method 1900 can include accessing, by the dataprocessing system 9300, a plurality of record objects. The dataprocessing system 9300 can access the record objects for a plurality ofdifferent systems of record, as described above in relation to FIG. 17.For example, the data processing system 9300 can make a call to thesystems of record 9360 that are associated with each of the data sourceproviders from which the data processing system 9300 retrievedelectronic activities at BLOCK 1902. The data processing system 9300 cangenerate a copy of the accessed record objects. The data processingsystem's copy of the access record objects can be referred to as shadowrecord objects.

As described above in relation to FIG. 10, each of the record objectscan be of a record object type. For example, the record objects can belead record objects, account record objects, opportunity record objects,or contact record objects. The record objects can be any type of recordobject in a system of record. The other systems of records can includeApplicant Tracking Systems (ATS), such as Lever, located in SanFrancisco, Calif. or Talend by Talend Inc., located in Redwood City,Calif., enterprise resource planning (ERP) systems, customer successsystems, such as Gainsight located in Redwood City, Calif., and DocumentManagement Systems, among others.

The data processing system 9300 can retrieve the record objects fromservers that correspond to the data source provider or data source fromwhich the data processing system 9300 retrieved the electronicactivities 9305. The data processing system 9300 can retrieve the recordobjects 1602 from a system of record 9360. The data processing system9300 can retrieve the record objects 1602 through an API call. Forexample, the data processing system 9300 can retrieve a first pluralityof record objects corresponding to a first system of record of a firstdata source provider and second plurality of record objectscorresponding to a second system of record of a second data sourceprovider.

As described above in relation to FIG. 17, among others, the system canbe configured in a multi-tenant configuration. In a multi-tenantconfiguration, the data processing system 9300 can retrieve a respectiveplurality of record objects that correspond to each of the data sourceprovider (e.g., tenants) associated with the data processing system9300. For example, the data processing system 9300 can retrieve aplurality of record objects from a system of record for each of the datasource providers.

Each of the record objects can include one or more object fields andcorresponding object field values. For example, the record objects canbe data structures and the object field values can be values of objectfields of the data structure. For example, for a contact record object,the data structure can include fields such as, but not limited to, name,address, email, and phone number, which can be filled with respectivefield values.

The method 1900 can include identifying an electronic activity (BLOCK1906). The method 1900 can include identifying, by the data processingsystem 9300, an electronic activity of the plurality of electronicactivities to match to one or more record objects. The data processingsystem 9300 can identify an electronic activity that is a candidate formatching to one or more record objects. The data processing system 9300can determine that an electronic activity is a candidate for matching toone or more record objects based on the filtering and exclusion rules.For example, if the electronic activity is identified by one or morefiltering or exclusion rules the electronic activity can be disregardedfrom consideration for matching to a record object.

The data processing system 9300 can identify electronic activities ascandidates based on one or more tags applied to the electronic activity.The above-described tagging engine 265 can assign one or more tags tothe electronic activity when the electronic activity is ingested orprocessed. For example, if all the participants associated with theelectronic activity are internal (e.g., each participant has an emailaddress with the domain of the data source provider), the tagging engine265 can tag the electronic activity as internal. The data processingsystem 9300 can be configured such that electronic activities tagged asinternal are not matched to record objects. In another example, if theelectronic activity includes a participant that is associated with anaccount record object, the data processing system 9300 can tag theelectronic activity as a candidate for matching.

As described above in relation to FIGS. 5A-5C and 6B, the dataprocessing system 9300 can identify and extract content from theelectronic activities. For example, the data processing system 9300 canidentify participants associated with the electronic activity. Theparticipants can be the sender or the receiver of the electronicactivity. The data processing system 9300 can identify the participantsassociated with the electronic activity by identify the sender's emailaddress and the recipient's email address.

In some embodiments, the data processing system 9300 can assign one ormore tags to the electronic activity. The data processing system 9300can assign tags to the electronic activities based on the contentincluded in the electronic activity or the metadata therefor. Forexample, the tags can be based on one or more character stringsidentified in the body of the electronic activity, in the metadata ofthe electronic activity, or in related electronic activities.

For example, the electronic activity can be an email message and thedata processing system 9300 can identify keywords within the email'sbody. The keywords can be identified by the above-described taggingengine 265. The keywords can identify the subject matter, phrases,accounts, topics, identification numbers, or other terms in or relatedto the subject of the electronic activity.

The method 1900 can include determining a data source provider (BLOCK1908). The method 1900 can include the data processing system 9300 fromwhich of the data source providers, the data processing system 9300received the electronic activity. For example, the data processingsystem 9300 can receive electronic activity from a plurality of datasource providers. In some embodiments, when the data processing system9300 receives the electronic activity, the electronic activity can labelor store the electronic activity in a database in association of thedata source provider that provided the electronic activity.

The method 1900 can include identifying a system of record (BLOCK 1910).The data processing system 9300 can identify a system of record thatcorresponds to the data source provider that the data processing system9300 identified at BLOCK 1908. The data processing system 9300 canidentify a plurality of candidate record objects that are associatedwith the data source provider. For example, and referring to FIGS. 3, 4,and 16-18, among others, once the data processing system 9300 identifiesa system of record 9360, the data processing system 9300 can identifythe record objects in the system of record 9360 as candidate recordobjects to which the electronic activity can be matched. The dataprocessing system 9300 can match the electronic activity with one ormore of the record objects in the system of record 9360.

In some embodiments, the data processing system 9300 can identify theshadow record objects in the shadow system of record as candidate recordobjects. For example, and referring to FIG. 3, the system of record fromeach data source provider can be copied into the data processing system9300 as shadow system of record 9330. Each of the shadow systems ofrecord 9330 can include a plurality of record objects that are shadowrecord objects of the record objects in the data source providers systemof record 9360. The data processing system 9300 can match the electronicactivity to one of the identified shadow record objects. The dataprocessing system 9300 can directly match the electronic activity to oneor more record objects in the shadow system of record 9330, one or morerecord objects in the system of record 9360, or one or more recordobjects in both the shadow system of record 9330 and the system ofrecord 9360 subject to limitations of the system of record 9360. In someembodiments, the data processing system 9300 can match the electronicactivity to one or more record objects in the shadow system of record9330, which can then be synced with the record objects in the system ofrecord 9360. In some embodiments, the data processing system 9300 canmatch the electronic activity to more than one record object in theshadow system of record 9330. In some such embodiments, the dataprocessing system 9300 can determine the shadow record object with whichthe electronic activity most closely matches (or has the highest matchscore) and cause the electronic activity to match the correspondingrecord object in the system of record 9360.

In some embodiments, each of the electronic activities can be associatedwith a domain. For example, the domain can be identified by the sendingemail address of the electronic activity. The data processing system9300 can identify the system of record based on a domain associated withan email address of the sender of the electronic activity.

The method 1900 can include determining whether the electronic activitycan be matched to a record object (BLOCK 1912). The data processingsystem 9300 can determine if the electronic activity can be matched to arecord object by applying a first policy. The policy can include one ormore filtering rules.

For example, and also referring to FIGS. 4 and 18 among others, thefiltering engine 270 can first process the electronic activity withfiltering rules 1701 to determine whether the electronic activity shouldbe blocked, removed from further processing, redacted, or deleted fromthe data processing system 9300.

The above described filtering engine can determine the electronicactivity should not be matched to a record object based on one or morefiltering rules. The filtering rules can restrict the data processingsystem 9300 from performing further processing or matching on theelectronic activity. The filtering rules can include a keyword ruleconfigured to restrict electronic activities including a predeterminedkeyword; a regex pattern rule configured to restrict electronicactivities including one or more character strings that match apredetermined regex pattern; a logic-based rule configured to restrictelectronic activities based on the participants of the electronicactivities satisfying a predetermined group of participants; or anycombination thereof.

The filtering rules can be defined by the data source provider of theelectronic activity and the system of record to which to match theelectronic activity. For example, the data source provider can definerules for electronic activities that should not be matched to the recordobjects in its system of record 9360.

In some embodiments, the filtering engine 270 can restrict electronicactivities from being matched to a record object by applying one or morerules to the electronic activity to identify the electronic activitiesthat should not be matched with a record object. The rules can includedetermining that the electronic activity includes one or morepredetermined words included in a list of restricted words. For example,electronic activities that include terms or phrases related to aspecific product identified by the data source provider or department(e.g., legal department) associated with the data source provider can beidentified by the filtering engine 270 for restriction from furtherprocessing.

In some embodiments, the filtering engine 270 can restrict electronicactivity from being matched with a record object if the electronicactivity includes any character strings that has a regular expressionpattern that matches a predefined regex pattern included in a list ofrestricted regex patterns. For example, the filtering engine 270 caninclude a list of restricted regex patterns that can include a patternto identify social security numbers, bank account numbers, credit cardnumbers, dates of birth, or other sensitive information.

The filtering engine 270 can restrict electronic activity from beingmatched with a record object by determining that the sender of theelectronic activity match a sender included in a list of restrictedsender list. For example, the email address of the company's generalcounsel can be included on a restricted sender list and all of theemails sent by the general counsel will be restricted out by thefiltering engine 270. The filtering engine 270 can restrict electronicactivity from being matched with a record object by determining that arecipient of the electronic activity matches a recipient included in arestricted recipient list. For example, the filtering engine 270 mayrestrict out any email or electronic activity sent to a human resourcemanager. The filtering rules 1701 can include one or more rule sets. Therules in the filtering rules 1701 can be defined by the data processingsystem 9300. The rules can be global rules that the data processingsystem 9300 can apply to the electronic activities of each data sourceprovider. The data processing system 9300 can include semi-global rulesthat are applied to the electronic activities from a subset of the datasource providers. For example, the data processing system 9300 can havefinance semi-global rules that are applied to the electronic activitiesfrom data source providers involved in the business of finance. Therules can be defined or otherwise configured by the data source providerand applied to only the electronic activities associated with the datasource provider.

The filtering engine 270 can restrict electronic activity from beingmatched with a record object based on a sender-recipient pair. Forexample, the filtering engine 270 can include a restriction list thatincludes a plurality of sender-recipient pairs. When a sender of theelectronic activity sends an electronic activity to one of therecipients with which the sender is paired in the restriction list, thefiltering engine can restrict out the electronic activity.

If the filtering engine 270 does not restrict the electronic activityfrom further processing by identifying the electronic activity with thefiltering rules 1701, the data processing system 9300 can determine thatthe electronic activity should be matched with one of the candidaterecord objects associated with the data source provider.

The method 1900 can include identifying candidate record objects (BLOCK1914). The data processing system 9300 can identify one or morecandidate record objects to which the electronic activity can bematched. For example, as described above in relation to FIG. 12, theelectronic activity can be matched to a plurality of record objects. Thedata processing system 9300 can identify the candidate record objectsbased on applying a second policy. The second policy can include one ormore rules for identifying candidate record objects based on one or moreparticipants of the electronic activity.

Also referring to FIGS. 11, 12, and 18, among others, the dataprocessing system 9300 can identify the plurality of record objects towhich the electronic activity can be matched based on one or more rulesor rule sets. The rules that identify to which of the record objects thedata processing system 9300 can match the electronic activity can beincluded in a second policy that includes one or more rule sets. Thedata processing system 9300 can identify the plurality of record objectsbased on one or more tags assigned to the electronic activity by thetagging engine 265.

As described above, the electronic activity linking engine 250 canidentify one or more candidate record objects to match the electronicactivity using recipient-based rules that identify the candidate recordobjects based on one or more recipients of the electronic activity. Therecipient-based rules can include rules for identifying the recipientbased on a specific recipient (e.g., based on an email address). Therecipient-based rules can include rules for identifying the recipientbased on data associated with the recipient. For example, the rule canidentify recipients having a predetermined domain in their emailaddress. An indication of the recipient can be included in theidentified record object as a value in an object field.

The electronic activity linking engine 250 can identify one or morecandidate record objects to match the electronic activity usingsender-based rules that can identify the candidate record objects basedon the sender of the electronic activity. The sender-based rules caninclude rules for identifying the record object based on a specificsender or based on data associated with the sender. An identification ofthe sender can be included in the identified record object as a value inan object field.

In some embodiments, the electronic activity linking engine 250 canidentify the candidate record objects based on sender-based rules orrecipient-based rules or both. For example, and referring to FIG. 11,the electronic activity linking engine 250 can select a first group ofcandidate record object using the recipient-based rules and a secondgroup of candidate record objects using the sender-based rules. Theelectronic activity linking engine 250 can match the electronic activityto one of the candidate record objects that is included in the both thefirst group of record objects and the second group of record objects.

In some embodiments, the matching rules can be configured to selectrecord objects of a specific type. For example, and also referring toFIGS. 10-12, among others, the matching rules can include a first set ofrules that identify account record objects, a second set of rules thatidentify opportunity record objects, and a third set of rules thatidentify lead record objects.

Each of the matching rules can have a priority level, score, or weight.The candidate record objects selected with rules with a higher prioritylevel can be assigned a higher score. For example, if the rules selectmultiple record objects, the electronic activity linking engine 250 canselect the candidate record object with the highest score. In someembodiments, a candidate record object can be selected multiple times.For example, a first and a second matching rule can each select a givenrecord object. The record object selected by multiple matchingstrategies can be given an aggregate (for example, a weighted aggregate)of the scores associated with each of the matching rules that selectedthe candidate record object.

The data source provider can assign the priority level, score, rank, orweight to each of the matching rules. For example, the data sourceprovider can assign a first priority level to a first subset of thematching rules and a second priority level to a second subset of thematching rules.

Also referring to FIGS. 5A-9, among others, the electronic activitylinking engine 250 can identify candidate record objects based onmatching rules that can identify record objects based on an object fieldvalue of the record object that identifies one or more nodes. One ormore participants of the electronic activity can be used to select anode of a node graph.

In some embodiments, the rules can candidate record objects based onparticipants that are linked to a record object. For example, an accountrecord object can include an object field that can include a pluralityof values. The object field values can identify nodes of a node graph.The data processing system 9300 can, using the matching rules, selectcontact record objects that are associated with identified nodes of thenode graph. In some embodiments, the candidate record objects can beidentified based on one or more of the participants associated with theelectronic activity being identified in the object field value.

The object field of the record object can identify an object owner orteam, which can be user, contact, or team that is responsible for theaccount associated with a record object. Based on the values, the dataprocessing system 9300 can identify a plurality of contact recordobjects that are associated with the object as candidate record objects.

The data processing system 9300 can identify candidate record objectsbased on one or more tags assigned to the electronic activity. Thetagging engine 265 and the tagging of electronic activity is describedabove in Section G, among others. The tagging engine 265 can tag theelectronic activity as specifically mentioning an account, product,contact, lead, or as including another predetermined character string.One or more of the rules can select candidate record objects based onthe selecting record objects associated with the one or more tags of theelectronic activity. For example, a predetermined account tag can beapplied to an email if the body of the email includes an identificationof the tag and the data processing system 9300 can identify the accountrecord object associated with the account tag as a candidate recordobject. In another example, the electronic activity can be parsed andthe term “renewal” can be identified in the electronic activity. A“renewal” tag can be applied to the electronic activity. A matching ruleto select record objects based on tags can select a renewal recordobject opportunity with the electronic activity and identify the renewalrecord object opportunity as a candidate record object. As describedabove in relation to FIG. 12, an indication of each of the recordobjects identified by a matching rule can be stored in a record objectarray 1202.

The method 1900 can include selecting a record object (BLOCK 1916). Alsoreferring to FIG. 12, among others, the data processing system 9300 caninclude identify candidate record objects to which the electronicactivity can be matched. As illustrated in FIG. 12, the matching rulescan identify more than one candidate record objects. The electronicactivity linking engine 250 can select one or more of the candidaterecord objects with which to match the electronic activity.

The electronic activity linking engine 250 can select the one or morerecord objects from the plurality of candidate record objects based onthe priority level used to select or identify each of the plurality ofcandidate record objects. For example, as described above in relation toFIG. 11, among others, each of the matching rules can have a prioritylevel, score, or weight. The candidate record objects selected withrules with a higher priority level can be assigned a higher score. Forexample, if the rules select multiple record objects, the electronicactivity linking engine 250 can select the candidate record object withthe highest score. In some embodiments, a candidate record object can beselected multiple times. For example, a first and a second matching rulecan each select a given record object. The record object selected bymultiple matching strategies can be given an aggregate (for example, aweighted aggregate) of the scores associated with each of the matchingrules that selected the candidate record object.

The data source provider can assign the priority level, score, rank, orweight to each of the matching rules. For example, the data sourceprovider can assign a first priority level to a first subset of thematching rules and a second priority level to a second subset of thematching rules.

The method 1900 can include storing an association between the selectedcandidate record object and the electronic activity (BLOCK 1918). Forexample, the data processing system 9300 can store, in a data structure,an association between the selected candidate record objects and theelectronic activity. Also referring to FIGS. 3 and 4, among others, theelectronic activity can be matched to one or more candidate recordobjects that are record objects in a shadow system of record for thedata provider that provided the electronic activity.

In some embodiments, once the electronic activity is matched with one ormore record objects, the data processing system 9300 can identifysubsequent electronic activities that are related to the matchedelectronic activities. For example, the data processing system 9300 canidentify emails that are part of the same email chain. The dataprocessing system 9300 can match each of the emails in the email chainto the one or more record objects to which the first email was matched.

In some embodiments, the electronic activity linking engine 250 candetect changes in the stored associations between electronic activitiesand record objects. Once the electronic activity is matched to a recordobject a user can accept, reject, or update the linking between theelectronic activity and the matched record object. The user can manuallyremap the linking of the electronic activity from a first record objectto a second, different record object. In another example, the dataprocessing system 9300 may automatically rematch electronic activitiesat predetermined intervals or when the data processing system 9300receives additional data.

In some embodiments, when the electronic activity linking engine 250determines that the electronic activity is matched with a second,different record object, the electronic activity linking engine 250 canupdate the matching rules or policies that matched the electronicactivity to the original record object. The electronic activity linkingengine 250 can update the matching rules or policies such that thesubsequent electronic activities are correctly matched with the correctrecord object.

16. Matching Electronic Activities to Record Objects of Systems ofRecord with Node Profiles

As described above, the system described herein can match electronicactivities with one or more record objects. The system can match theelectronic activities in a single-tenant or multi-tenant configurationof the system. For example, in a single-tenant configuration, the systemcan receive or access electronic activities from a single data sourceprovider and match the electronic activities to record objects of asystem of record of the data source provider from which the electronicactivities were received or accessed. In a multi-tenant configuration,the system can receive or access electronic activities from multipledata source providers and match the electronic activities to recordobjects of a system of record of the respective data source providerfrom which the electronic activities were received or accessed. Asdescribed herein, the system can automatically match, link, or otherwiseassociate the electronic activities with one or more record objects. Insome embodiments, the system can match the electronic activity with oneor more node profiles. The system can use the node profiles to identifyone or more record objects with which the electronic activity can bematched. If the system determines the electronic activity is eligible orqualifies to be matched with one or more record objects, the system canmatch the electronic activity to one or more of the record objectsidentified with the node profiles using one or more set of rules or rulesets. The system can then rank the identified candidate record objectsto select one or more record objects with which to associate theelectronic activity. The system can then store an association betweenthe electronic activity and the selected one or more record objects.

FIG. 20 illustrates a block diagram of an example process flow 2000 forprocessing electronic activities. Also, with reference to FIGS. 3, 4,and 16-18, among others, the data processing system 9300 can be incommunication with one or more data source providers 9350. Each of thedata source providers 9350 can include a data source 9355. FIG. 20illustrates an example of a single-tenant system where the electronicactivities 9305 from a single tenant (e.g., the data source provider9350 that includes the data source 9355) is matched to the recordobjects 1602 of a single shadow system of record 9330. The single shadowsystem of record 9330 can be associated with the data source provider9350 that provided the electronic activity. For example, the shadowsystem or record can include data retrieved from the record objects ofthe data source providers system of record.

As illustrated in FIG. 17, among others, the system illustrated in FIG.20 can be a multi-tenant system that can include a plurality of datasources 9355 that can each include a plurality of electronic activities9305. The system can match the electronic activities with record objectsin shadow systems of record 9330 or directly with systems of record 9360associated with the respective data source 9355.

The data source provider 9350 can store electronic activity9305(1)—electronic activity 9305(N) (generally referred to as electronicactivity 9305) in the data source 9355. As described above, theelectronic activities can include one or more forms of electronicactivity, such as email or other forms of electronic communication. Thedata processing system 9300 can access or otherwise retrieve theelectronic activity 9305 from the data source 9355. For example, theabove-described electronic activity ingestor 205 can be configured toingest electronic activities in a real-time or near real-time basis foraccounts of one or more enterprises, organizations, companies,businesses, institutions or any other group associated with the datasource providers. The electronic activity ingestor 205 can ingestelectronic activities. For example, when a data source providersubscribes to a service provided by the data processing system 9300, thedata source provider can provide access to electronic activitiesmaintained by the data source provider by going through an onboardingprocess. That onboarding process can enable the data processing system9300 to access electronic activities owned or maintained by the datasource provider in one or more data sources 9355. For example, the datasources 9355 can be, but are not limited to, mail servers, one or moresystems of record, one or more phone services or servers of the datasource provider, among other sources of electronic activity. Theelectronic activities ingested during an onboarding process may includeelectronic activities that were generated in the past, perhaps manyyears ago, that were stored on the electronic activities' sources. Thedata processing system 9300 can be configured to ingest (and re-ingest)the electronic activities from one or more data sources 9355 on aperiodic basis, including daily, weekly, monthly, or any reasonablefrequency.

The data processing system 9300 can match the electronic activities 9305with one or more node profiles 715. For example, and also referring toFIGS. 3-9, among others, the node graph generation system 200 cangenerate a node graph that includes a plurality of nodes. Each of thenodes can include a node profile 715, which can be a data structure thatincludes a plurality of fields. For example, an example node profile 715can include fields such as, but not limited to name, email, phone,company, and job title. The system can ingest electronic activities andpopulate the fields with values. Also referring to FIG. 4, among others,as the system ingests additional emails the node profile manager canupdate the node profile 715. The node profile managed can update thenode profile 715 by, for example, increasing or decreasing a confidencescore of the values of fields that can be verified or contradicted bysubsequent electronic activities. The node profile manager can addadditional (e.g., updated) values to a field based on ingestedelectronic activities.

When matching an ingested electronic activity 9305 to a record object1602, the data processing system 9300 can match the electronic activity9305 with one or more node profiles 715. For example, the dataprocessing system 9300 can identify or parse the sender and recipientemail addresses from an email (an example electronic activity) andidentify a first node profile 715 based on the senders email address anda second node profile 715 based on the recipient's email address. Asillustrated in FIG. 20, the electronic activity 9305(1) can be matchedwith a first and a second node profile 715. For example, one of the nodeprofiles 715 can be associated with the sender and one of the nodeprofiles 715 can be associated with the recipient of the electronicactivity 9305(1). Additional details relating to matching electronicactivities to node profiles are described herein in Section 17 and thedescriptions above with respect to FIGS. 2-9.

The data processing system 9300 can match the electronic activities 9305to one or more record objects 1602 of the shadow system of record 9330using the node profiles 715 to which the electronic activity 9305 wasmatched. For example, and also referring to FIG. 6A, among others, thedata processing system 9300 can use one or more values 620 from one ormore fields 610 to identify candidate record objects. In someembodiments, the node profiles 715 can include additional informationthat isn't extracted from the given electronic activity 9305 beingmatched to a record object. In this example, matching the electronicactivity to a node profile 715 can enable the identification ofadditional record objects that may not be identified when using onlydata extracted from the electronic activity 9305. As illustrated in FIG.20, a first node profile 715 is matched to a first and second recordobject 1602 and a second node profile 715 is matched to the first and athird record object 1602. Each of the node profiles 715 that are matchedwith a given electronic activity 9305 can match to the same recordobjects, different record objects, or first and second sets of recordobjects that at least partially intersect with one another.

The record objects 1602 of the shadow system of record 9330 can besynced with the record object 1602 of the system of record 9360. Syncingthe shadow record objects 1602 with the record objects 1602 of thesystem of record 9360 can include adding values from fields of theshadow record objects 1602 to the corresponding values, such as matchedelectronic activities 9305, of the record objects 1602 in the system ofrecord 9360. In some embodiments, the data processing system 9300 canmatch the electronic activities 9305 directly to the system of record9360. For example, the data processing system 9300 can match theelectronic activities 9305 to the record objects in the system of record9360 without matching the electronic activities 9305 to the recordobjects in the shadow system or record 9330.

FIG. 21 illustrates a block diagram of an example method 2100 to matchelectronic activities to record objects of systems of record with nodeprofiles. The method 2100 can include maintaining a plurality of nodeprofiles (BLOCK 2102). The method 2100 can include maintaining, by oneor more processors of the data processing system 9300, a plurality ofnode profiles. Also referring to FIG. 6A, among others, each of the nodeprofiles can correspond to a different unique entity, such as a personor company. Each of the node profiles can include a plurality of fields,such as, but not limited to name, email address, company, domain,telephone number. Each of the fields can include one or more value datastructures. Each of the value data structures can include node fieldvalue and one or more entries corresponding to respective data pointsthat support the node field value of the value data structure. Forexample, and also referring to FIG. 6A among others, value datastructure 615 can include a value 620, an occurrence metric 625, aconfidence score 630 and one or more entries 635 a-n. The entries 635can include data (or an indication thereof) the basis for the value 620,the occurrence metric 625, and the confidence score 630.

For example, each entry 635 can identify a data source 640 from whichthe value was identified (for instance, a source of a system of recordor a source of an electronic activity), a number of occurrences of thevalue that appear in the electronic activity, a time 645 associated withthe electronic activity (for instance, at which time the electronicactivity occurred) and an electronic activity unique identifier 502identifying the electronic activity. In some embodiments, the occurrencemetric 625 can identify a number of times that value is confirmed oridentified from electronic activities or systems of record. The nodeprofile manager 220 can be configured to update the occurrence metriceach time the value is confirmed. In some embodiments, the electronicactivity can increase the occurrence metric of a value more than once.For instance, for a field such as name, the electronic activity parsercan parse multiple portions of an electronic activity. In someembodiments, parsing multiple portions of the electronic activity canprovide multiple confirmations of, for example, the name associated withthe electronic activity.

The method 2100 can include accessing a plurality of electronicactivities (BLOCK 2104). The method 2100 can include accessing, by theone or more processors, a plurality of electronic activities transmittedor received via electronic accounts associated with one or more datasource providers. The data processing system 9300 can update the nodeprofiles using the electronic activities. With reference to FIGS. 16-20,among others, the data processing system 9300 can access a plurality ofelectronic activities. The electronic activities can be transmitted tothe data processing system 9300 from data source providers. The dataprocessing system 9300 can retrieve the electronic activities from thedata source providers. For example, the data source provider can includeor be an email server. The data processing system 9300 can have theauthority to access the emails stored on the email server through an APIor an HTTP method (e.g., a GET method).

As described herein in relation to FIGS. 4-8, among others, the dataprocessing system 9300 can update the node profiles based on theaccessed electronic activities. For example, the node profile manager220 can maintain a node profile 715 for each unique entity, such as aperson or company. As the data processing system 9300 ingests electronicactivities, the node profile manager can update the node profile 715.The node profiles can be updated by changing one or more confidencescores of respective values corresponding to respective value datastructures by adding additional data points to the value data structurethat support the corresponding value. Furthermore, if a particular valueof a field of a node profile doesn't exist, the node profile manager canadd one or more additional values and corresponding value datastructures to the field. The increase or decrease in the confidencescore of values of fields can be based on the electronic activity. Forexample, when an electronic activity, such as an email is successfullytransmitted to the intended destination, the node profile manager 220can update the confidence score of the recipient email value. The dataprocessing system 9300 can determine the email was successfullytransmitted to the recipient, for example, if a bounce back email is notreceived in response to the email.

The method 2100 can include maintain one or more record objects (BLOCK2106). The method 2100 can include maintaining, by the one or moreprocessors, a plurality of record objects for one or more systems ofrecord. Each of the record objects of the plurality of record objectscan include one or more object fields having one or more object fieldvalues. As described above in relation to FIG. 20, among others, thedata processing system 9300 can make a call to the systems of record9360 that are associated with each of the data source providers fromwhich the data processing system 9300 retrieved electronic activities.The data processing system 9300 can generate a copy of the accessedrecord objects. The data processing system's copy of the access recordobjects can be referred to as shadow record objects. The data processingsystem 9300 can update the shadow record objects and sync the changesback to the record objects in the tenant's system of record.

As described above in relation to FIG. 10, each of the record objectscan be of a record object type. For example, the record objects can belead record objects, account record objects, opportunity record objects,or contact record objects. The record objects can be any type of recordobject in a system of record. The other systems of records can includeApplicant Tracking Systems (ATS), such as Lever, located in SanFrancisco, Calif. or Talend by Talend Inc., located in Redwood City,Calif., enterprise resource planning (ERP) systems, customer successsystems, such as Gainsight located in Redwood City, Calif., and DocumentManagement Systems, among others.

The data processing system 9300 can retrieve the record objects fromservers that correspond to the data source provider or data source fromwhich the data processing system 9300 retrieved the electronicactivities 9305. The data processing system 9300 can retrieve the recordobjects 1602 from a system of record 9360. The data processing system9300 can retrieve the record objects 1602 through an API call. Forexample, the data processing system 9300 can retrieve a first pluralityof record objects corresponding to a first system of record of a firstdata source provider and second plurality of record objectscorresponding to a second system of record of a second data sourceprovider.

As described herein the system can be configured in a multi-tenantconfiguration or a single-tenant configuration. In a multi-tenantconfiguration, the data processing system 9300 can retrieve a respectiveplurality of record objects that correspond to each of the data sourceprovider (e.g., tenants) associated with the data processing system9300. For example, the data processing system 9300 can retrieve aplurality of record objects from a first system of record and from asecond system of record.

Each of the record objects can include one or more object fields andcorresponding object field values. For example, the record objects canbe data structures and the object field values can be values of objectfields of the data structure. For example, for a contact record object,the data structure can include fields such as, but not limited to, name,address, email, and phone number, which can be filled with respectivefield values.

The method 2100 can include extracting data from an electronic activity(BLOCK 2108). The method 2100 can include extracting, by the one or moreprocessors, data included in an electronic activity of the plurality ofelectronic activities. For example, and referring to FIGS. 5A-5C amongothers, the data processing system 9300 can one or more recipients 510,one or more senders 512 of the electronic activity. The data processingsystem 9300 can identify a subject line 514, an email body 516, an emailsignature 518, and a message header 520 of the electronic activity. Themessage header can include additional information relating to thetransmission and receipt of the email message, including a time at whichthe email was sent, a message identifier identifying a message, an IPaddress associated with the message, a location associated with themessage, a time zone associated with the sender, a time at which themessage was transmitted, received, and first accessed, among others. Theelectronic message 505 can include additional data in the electronicmessage 505 or in the header or metadata of the electronic message 505.In some embodiments, the electronic activity can be an email, a callentry, a calendar entry, among others.

The method 2100 can include matching the electronic activity to a nodeprofile (BLOCK 2110). The method 2100 can include matching, by the oneor more processors, the electronic activity to at least one node profileof the plurality of node profiles. The data processing system 9300 canmatch the electronic activity to the one or more node profiles based ondetermining that the extracted data of the electronic activity and theone or more values of the fields of the at least one node profilesatisfy a node profile matching policy. For example, as describedherein, each value in a value data structure can include a confidencescore.

In some embodiments, the data processing system 9300 can identify asender and one or more recipients of the electronic activity. Forexample, the data processing system 9300 can extract from the electronicactivity the sender's email address and the email addresses of the oneor more recipients of the electronic activity. The data processingsystem 9300 can match the electronic activity to a plurality of nodeprofiles. For example, the data processing system 9300 can match theelectronic activity to a first node profile based on the sender's emailaddress. The data processing system 9300 can also match the electronicactivity to one or more additional node profiles based on the extractedrecipient email addresses. In some embodiments, strings or values areextracted from electronic activities and associated with candidate orpotential fields to form field-value pairs. These field-value pairsextracted from an electronic activity can then be compared withcorresponding field-value pairs of node profiles to identify or computea match score between the electronic activity and respective nodeprofiles having the field-value pairs with which the field-value pairsof the electronic activity are compared.

In some embodiments, the matching policies for the matching of theelectronic activity to one or more node profiles can be based on tagsassociated with the electronic activity. For example, the dataprocessing system 9300 can determine a relationship between two or morenode profiles based on the one or more values of the fields of the twoor more node profiles. The data processing system 9300 can assigning oneor more tags to the electronic activity based on the relationshipbetween the two or more node profiles. In one example, the dataprocessing system 9300 can assign a personal tag to the electronicactivity. For example, the node profile manager 220 can be configured todetermine that two node profiles have a personal (non-professional)relationship based on the electronic activities exchanged between theusers associated with the node profiles and apply a “personal” tag tothe emails between the users. The system can further determine aconfidence score for the tag classifying the two node profiles based onhow confident the system is in the prediction that the two node profileshave a personal relationship. In some embodiments, the node profilemanager 220 can further determine if two nodes have a personalrelationship based on commonalities in values in their node profiles,for instance, their home addresses (if they are neighbors), college orschool affiliations (alumni/classmates), same last names, othernon-professional affiliations, or other signals that may indicate thetwo node profiles may have a personal relationship. In some embodiments,the data processing system 9300 can determine to not match an electronicactivity that is associated with a personal tag to one or more recordobjects.

The data processing system 9300 can assign one or more tags to theelectronic activity based on one or more policies. The data processingsystem 9300 assign the tags based on one or more node profilesassociated with a sender or one or more recipients of the electronicactivity. For example, the data processing system 9300 can identify,based on the body of the electronic activity, that the electronicactivity is related to a sales deal and can tag the electronic activitywith a sales tag. The data processing system 9300 can assign tags basedon a relationship between the one or more node profiles associated withthe sender and the one or more recipients of the electronic activity.For example, as described herein the data processing system 9300 candetermine whether the users associated with the node profiles have aprofessional or personal relationship and assign a professional orpersonal tag to the electronic activity, accordingly. The dataprocessing system 9300 can assign tags to the electronic activity basedon one or more strings identified in the electronic activity. Forexample, the data processing system 9300 can parse the body of the emailwith regex to identify an account number and the data processing system9300 can assign a tag based on the account number. The data processingsystem 9300 can assign the tags to the electronic activity based on oneor more strings identified in the metadata of the electronic activity.For example, the metadata can be a header of an email and can include adomain associated with the sender of the electronic activity and thedata processing system 9300 can assign a tag based on the domainidentified in the header of the email.

The data processing system 9300 can match the electronic activity to oneor more node profiles based on contribution scores. For example, eachdata point for a value in a value data structure can include acontribution score. The contribution score can indicate the contributionof the data point to the value. The data point's contribution score canbe time dependent. For example, as described in relation to FIG. 7,among others, the contribution of the data point can decrease over time.In one example, a data point can have a greater contribution score ifthe data point was recently updated or generated when compared to a datapoint that was updated or generated in the past. Based on thecontribution scores for each of the data points associated with thevalue, the data processing system 9300 can calculate a confidence scoreof the value of the field of the node profile. The data processingsystem 9300 can select the node profiles based on the confidence scores.For example, the electronic activity may match to a plurality of nodeprofiles based on a value of a field in each of the node profiles. Thedata processing system 9300 can discard each of the node profiles ascandidate node profiles if the value of the field in a node profile hasa confidence score below a predetermined threshold.

In some embodiments, the contribution score of the data point can bebased on a trust score associated with the data source provider. Thedata processing system 9300 can determine, for a data point, acontribution score for the data point based on the trust scoreassociated with the data source provider. For example, a relatively lowtrust score can reduce the confidence score of the data point. The trustscore can be based on a type of source of the data point.

The method 2100 can include matching the electronic activity to one ormore record objects (BLOCK 2112). The method 2100 can include matching,by the one or more processors, the electronic activity to at least onerecord object of the plurality of record objects based on the extracteddata of the electronic activity and object values of the at least onerecord object. For example, the data processing system 9300 can identifya sender of the electronic activity. The data processing system 9300 canselect a first node profile of the plurality of node profiles based onthe sender of the electronic activity. For example, the data processingsystem 9300 can identify the email address of the sender and select anode profile based on the identified email address. Based on the nodeprofile, the data processing system 9300 can identify a first set ofrecord objects of the plurality of record objects. For example, and alsoreferring to FIG. 12, among others, the data processing system 9300 canidentify a plurality of account, opportunity, and lead record objectsbased on the email address of the sender. The data processing system9300 can also identify one or more record objects based on node fieldvalues contained in the identified node profile. For example, the nodeprofile can include a field for teams on which the user is assigned. Thefield can include a value in a value data structure for each of theteams on which the user is assigned. The data processing system 9300 canselect one or more record objects based on the teams identified in thenode profile.

The data processing system 9300 can identify one or more record objectsbased on a recipient of the electronic activity. For example, the dataprocessing system 9300 can identify a recipient email address of theelectronic activity. The data processing system 9300 can identify asecond node profile of the plurality of node profiles based on therecipient of the electronic activity. The data processing system 9300can identify a second set of record objects of the plurality of recordobjects based on the second node profile.

In some embodiments, the data processing system 9300 can filter or prunethe first set of record objects (e.g., the record objects selected basedon the sender of the electronic activity) and the second set of recordobjects (e.g., the record objects selected based on the recipient of theelectronic activity). For example, the data processing system 9300 canidentify an intersection of the first and second set of record objects(e.g., record objects that are included in both the first and second setof record objects). The data processing system 9300 can match theelectronic activity to at least one of the record objects in theintersection of the first set of record objects and the second set ofrecord objects.

In some embodiments, each of the record objects in the intersection ofthe first set of record objects and the second set of record objects canbe referred to as candidate record objects. The candidate record objectscan include one or more types of record object types. For example, therecord objects can be account record objects, opportunity recordobjects, or lead record objects, among others. The data processingsystem 9300 can match the electronic activity with one or more of therecord objects in candidate record objects. For example, the dataprocessing system 9300 can match the electronic activity to recordobjects in the candidate record objects that have different types. Forexample, the data processing system 9300 can match the electronicactivity to an account record object and an opportunity record object.In some embodiments, the data processing system 9300 can match theelectronic activity to multiple record objects with the same type. Forexample, the data processing system 9300 can match the electronicactivity to two candidate record objects that are both account recordobjects.

The method 2100 can include matching the electronic activity to one ormore of the identified record objects based on one or more matchingpolicies, rules, heuristic, or filters. The matching policies can bebased on the sender and/or recipient of the electronic activity. Thedata processing system 9300 can identify a first set of matchingpolicies based on the sender of the electronic activity and a second setof matching polices based on the recipients of the electronic activity.As described herein, the data processing system 9300 can identify afirst set of candidate record objects based on the first set of matchingpolicies and a second set of candidate record objects based on thesecond set of matching policies. The data processing system 9300 canidentify an intersection between the first and second sets of candidaterecord objects.

For example, and also referring to FIGS. 4 and 18 among others, thefiltering engine 270 can first process the electronic activity withfiltering rules 1701 to determine whether the electronic activity shouldbe blocked, removed from further processing, redacted, or deleted fromthe data processing system 9300. The above described filtering enginecan determine the electronic activity should not be matched to a recordobject based on one or more filtering rules. The filtering rules canrestrict the data processing system 9300 from performing furtherprocessing or matching on the electronic activity. The filtering rulescan include a keyword rule configured to restrict electronic activitiesincluding a predetermined keyword; a regex pattern rule configured torestrict electronic activities including one or more character stringsthat match a predetermined regex pattern; a logic-based rule configuredto restrict electronic activities based on the participants of theelectronic activities satisfying a predetermined group of participants;or any combination thereof.

The data processing system 9300 can also use one or more policies (e.g.,matching rules 1702) to select the candidate record objects. matchingpolicies can be defined by the data source provider of the electronicactivity and the system of record to which to match the electronicactivity. For example, the data source provider can define rules forelectronic activities that should not be matched to the record objectsin its system of record 9360. The rules can include determining that theelectronic activity includes one or more predetermined words included ina list of restricted words. For example, electronic activities thatinclude terms or phrases related to a specific product identified by thedata source provider or department (e.g., legal department) associatedwith the data source provider can be identified by the filtering engine270 for restriction from further processing.

In some embodiments, the data processing system 9300 can identify, usingnatural language processing, a string in the electronic activity. Thematching policies can include matching the electronic activity to one ormore record objects based on the string identified in the electronicactivity. For example, the data processing system 9300 can identify astring in the body of the electronic activity. The data processingsystem 9300 can identify the string using regex or other patternmatching technique. The string can include an account number or otheridentifier. The data processing system 9300 can select the candidaterecord objects based on the string in the body of the electronicactivity.

In some embodiments, the matching policies can match the electronicactivity to one or more record objects based on tags associated with theelectronic activity. The data processing system 9300 can identify afirst subset of record objects based on one or more tags assigned to theelectronic activity. The data processing system 9300 can then match theelectronic activity to at least one record object in the first subset ofthe record objects based on the one or more tags assigned to theelectronic activity.

The method 2100 can include storing an association between theelectronic activity and one or more record objects (BLOCK 2114). Themethod 2100 can include storing the association in a data structure.Also referring to FIGS. 3 and 4, among others, the electronic activitycan be matched to one or more candidate record objects that are recordobjects in a shadow system of record (or the system of record) for thedata provider that provided the electronic activity.

In some embodiments, once the electronic activity is matched with one ormore record objects, the data processing system 9300 can identifysubsequent electronic activities that are related to the matchedelectronic activities. For example, the data processing system 9300 canidentify emails that are part of the same email chain. The dataprocessing system 9300 can match each of the emails in the email chainto the one or more record objects to which the first email was matched.

In some embodiments, the data processing system 9300 can detect changesin the stored associations between electronic activities and recordobjects. Once the electronic activity is matched to a record object auser can accept, reject, or update the linking between the electronicactivity and the matched record object. The user can manually remap thelinking of the electronic activity from a first record object to asecond, different record object. In another example, the data processingsystem 9300 may automatically rematch electronic activities atpredetermined intervals or when the data processing system 9300 receivesadditional data.

As described herein, and in relation to the stage classification engine325, for example, once one or more electronic activities are matched toa record object (e.g., an opportunity record object), the dataprocessing system 9300 can classify a stage of the record object. Thestages can be a stage, step, or task in a business process, a salesprocess, a hiring process, a support ticket, or other workflow. Thestages can be defined by the system or by the data source provider. Forexample, the data processing system 9300 can identify at least a subsetof the plurality of electronic activities that are matched to a firstrecord object. The data processing system 9300 can also identify, foreach of the electronic activities matched with the record object, one ormore node profiles. The data processing system 9300 can determine astage of the first record object based on the identified one or morenode profiles of each of the subset of electronic activities.

Using the example of an opportunity record object in a sales process,the stages can indicate the steps taken in an opportunity or deal fromthe beginning of the deal to the final disposition of the deal (e.g.,close and won or closed and lost). The stages can include, but are notlimited to: prospecting, developing, negotiation, review, closed/won, orclosed/lost.

In some embodiments, the stages can be based on the contacts present orinvolved on one or more sides of the deal. For example, as the dealadvances to higher stages, more senior people may be included in theelectronic activities. The stage of the deal can be based on theidentification or introduction of the above-described OCR. The dataprocessing system 9300 can identify the OCR or other contacts present orinvolved on the deal based on the node profiles. For example, the dataprocessing system 9300 can identify the node profiles based matched withthe one or more electronic activities associated with a record object.Based on the node profiles, the data processing system 9300 candetermine each of the contacts roles, positions, or titles. For example,“title” can be one of the fields in the node profile. The dataprocessing system 9300 can use the field node value in the title fieldto determine the title of the person involved with the record object.The data processing system 9300 can also determine the stage of therecord object based on the one or more tags assigned to the electronicactivity associated with the record object.

In some embodiments, the data processing system 9300 can maintain anormalized set of stages. The normalized set of stages can be referredto as processor assigned stages. Each of the data source providers candefine custom stages for the record objects of the data source provider.Each stage (of the processor assigned stages or the data source providerassigned stages) can indicate a proximity to the completion of an event,task, process, or other workflow. The data processing system 9300 cangenerate a mapping between the data source provider assigned stages andthe processor assigned stages. For example, the stage classificationengine 325 can define five, normalized stages. A first data sourceprovider can define a deal or opportunity as including 7 stages. Asecond data source provider can define a deal or opportunity asincluding 3 stages. The stage classification engine 325, for the firstdata source provider, may map stages 1 and 2 to normalized stage 1,stage 3 to normalized stage 2, stage 4 to normalized stage 3, stage 5 tonormalized stage 4, and stages 6 and 7 to normalized stage 5.Accordingly, the data source provider's stages can be mapped to thenormalized stages based on the tasks, requirements, or content of thestages rather than by the naming or numbering of the stages.

17. Linking Electronic Activities to Node Profiles

The present solution can enable real-time or near real-time linking ofelectronic activities to node profiles, with increased accuracy. In somesystems that maintain data regarding entities, such as individuals orenterprises, including systems of record, the data may be self-reported,such as in response to specific queries to provide data for fields suchas first name, last name, title, or email. As such, this data may beinaccurate. For example, when the data was provided, the data may havebeen inaccurate due to the data being self-reported. At a particularinstant in time after the data was provided, due to changes to the datathat may have occurred subsequent to when the data was provided andbefore the data has been updated, the data even if it was previouslycorrect at the time the data was provided, may also eventually becomeobsolete, stale or inaccurate.

The present solution described herein can match electronic activities tonode profiles maintained by a node graph generation system, that can usethe data included in the electronic activities to update node profilesand the values of fields of these node profiles unobtrusively andwithout requiring any human input. As such, the present disclosuredescribes solutions for maintaining node profiles that remain accurateas the node profiles do not include self-reported information submittedby a user to update the node profile and because the node profiles areautomatically updated as electronic activities are ingested andprocessed by the system without requiring any human activity. In thisway, the present solution can enable dynamic updates to node profilesand a node graph including such node profiles, rather thanmanual/self-reported updates.

By linking electronic activities to node profiles, the present solutioncan increase the accuracy and validity of the node profiles, such as byincreasing a likelihood that each node profile represents the true stateof the world. For example, when node profiles are used to generate anode graph indicative of a hierarchy or other relationships amongst nodeprofiles, the present solution can more accurately represent values offields such as hierarchical titles within enterprises that are used togenerate the node graph. The present solution can more accurately rankeach value of each field (each value representing a potential true stateof the world) by dynamically updating the confidence score correspondingto each value responsive to extracting data from electronic activities,so that the present solution outputs an evidence-based estimation ofwhich value is the true value with improved accuracy. As an example, anode profile can include a first email address corresponding to a firstenterprise at which the user corresponding to the node profile wasemployed and a second email corresponding to a subsequent enterprise atwhich the user corresponding to the node profile was employed. Each ofthe two email addresses are at respective points in time, accurate andvalid. As the person switches jobs, the first email address is no longervalid but the confidence score associated with the first email addresscan in some embodiments, remain high indicating that the first emailaddress belongs to the node profile. Similarly, the second email addressalso belongs to the node profile and therefore also can have aconfidence score that may start low but increase as more electronicactivities including the second email address are processed by the dataprocessing system described herein. After the system determines that thesecond email address is active and functioning, the system canautomatically increase the confidence score of the second email addresssince the contribution scores provided by recent data points (forexample, recent electronic activities identifying the second emailaddress) can contribute towards the higher confidence score whileautomatically decreasing the confidence score of the first email addresssince the electronic activities supporting the first email address aregetting older and no new electronic activities serve as data points forthe first email address. The present solution can thus respond tochanges in the true state of the world represented by the node profileusing the second email, rather than relying on self-reported informationwhich may be inaccurate and/or delayed.

Referring further to FIG. 2, among other, the node graph generationsystem 200 can ingest electronic activities to generate or update nodeprofiles that are maintained by the node graph generation system 200using data from the electronic activities. For example, as illustratedin FIGS. 5A-5C and 6B, the node graph generation system 200 can processelectronic activities such as an email 505, a call entry 525, or acalendar entry 560. For example, the node graph generation system 200can process the email 505 to identify a plurality of strings having datafrom the To: field 510 (to identify a recipient of the email 505), theFrom: field 512 (to identify a sender of the email 505), the email body516 (to identify a recipient of the email 505), and the email signature518 (to identify the sender of the email 505).

Using the identified plurality of strings, the node graph generationsystem 200 can generate activity field-value pairs. Each activityfield-value pair can include a data structure that associates aparticular field to a value for the field that the node graph generationsystem 200 extracts from the electronic activity. For example, the nodegraph generation system 200 can generate a FirstName-value pairassociating a value of “John” to the first name field, a LastName-valuepair associating a value of “Smith” to the last name field, aTitle-value pair associating a value of “Director” to the title field,and a CompanyName-value pair associating a value of “ACME” to thecompany name field based on the email 652 a illustrated in FIG. 6B.Because each electronic activity may include multiple strings havingdata that corresponds to a particular field, the node graph generationsystem can generate multiple activity-field value pairs from eachelectronic activity (e.g., multiple first name-value field pairs basedon information from a sender field and a signature block).

Referring further to FIG. 6A, the node graph generation system 200 canmaintain a plurality of node profiles 600. Each node profile 600includes a plurality of node field-value pairs corresponding toattributes 610 and value data structures 615. For example, the nodegraph generation system 200 can maintain a first node field-value pairassociating a first value 620 (e.g., Va) to field 610(1), a second nodefield-value pair associating a second value 620 (e.g., Vb) to the field610(1), and so on for each value. As shown for the node profileillustrated in the table above, the node graph generation system 200 cangenerate a first field-value pair associating a value of John to thefirst name field, and a second field-value pair associating a value ofJohnathan to the first name field.

The node graph generation system 200 can compare the activityfield-value pairs of an electronic activity to be matched to respectivenode field-value pairs of one or more candidate node profiles with whichto match the electronic activity. The node graph generation system 200can compare one or more activity field-value pairs of the electronicactivity to corresponding node field-value pairs of a candidate nodeprofile to determine a match score between the electronic activity andthe candidate node profile. The node graph generation system 200 canidentify one or more node profiles with which to match the electronicactivity based on the match score. Node profiles having a match scorebelow a predetermined threshold can be determined not to be matched.

To compute the match score, the node graph generation system 200 caniterate through each activity field-value pair, identify the field ofthe activity field-value pair, and identify a corresponding field of anode field-value pair of the node profile. For example, the node graphgeneration system 200 can identify the field of the activity field-valuepair to be first name, and based on the identification, select the fieldof the node field-value pairs that will be used for the comparison to bethe first name field of the node field-value pairs. The node graphgeneration system 200 can retrieve the value from the activityfield-value pair, retrieve a corresponding value that is associated tothe identified field of the node field-value pair, and compare thevalues. For example, the node graph generation system 200 can select thefirst name field of a first activity field-value pair, identify acorresponding first name field of a first node field-value pair,retrieve the value of the first name from the first activity field-valuepair, retrieve the corresponding value of the first name from the firstnode field-value pair, and compare the retrieved values. With referenceto the electronic activity EA-003 and node profile NPID-12 of FIG. 6B,the node graph generation system can generate an activity field-valuepair of FirstName:John, identify the field to be first name, identifythe corresponding first name field of each node field-value pair of thenode profile NPID-12, retrieve the first name John from the activityfield-value pair, and retrieve the first name John from the nodefield-value pair (or the first name Johnathan from the second value thatis assigned to the first name field of the node profile NPID-12). Thenode graph generation system 200 can compare the first name John of theactivity field-value pair to the first name John of the node field-valuepair, and calculate a match score based on the comparison. For example,the node graph generation system 200 can assign a match score of 100percent based on the comparison of John and John. The node graphgeneration system 200 may assign a match score less than 100 percentbased on the comparison of John and Johnathan. The node graph generationsystem 200 can calculate match scores for each comparison of theelectronic activity and respective candidate node profiles.

The node graph generation system 200 can compare each match scorebetween the electronic activity and the node profile to a match scorethreshold to determine whether the electronic activity is to be matchedto the node profile. As such, the node graph generation system 200 canuse the data extracted from the electronic activity to make decisionssuch as whether an electronic account associated with the electronicactivity was a sender or a recipient of the electronic activity. Thenode graph generation system 200 can calculate an average (e.g.,weighted average) of each match score determined for each comparison forthe electronic activity, and compare the weighted average to the matchscore threshold to determine whether the electronic activity matches thenode profile.

The node graph generation system 200 can apply various rules todetermine how to calculate the weighted average. In some embodiments,the node graph generation system 200 calculates the weighted averagebased on a measure of uniqueness of the field of the value used tocalculate the match score. The node graph generation system 200 canapply different weights to different fields based on the rarity score ofthe field. The rarity score of the field can be determined by generatinga count of each value of the field across all node profiles maintainedby the node graph generation system. If a predetermined number orthreshold of values have a frequency count that satisfies apredetermined threshold, the field can have a lower rarity score thananother field in which none of the values have a frequency count thatexceeds the predetermined threshold. For example, the field FirstNamecan have a low rarity score because there are a lot of common firstnames, such as John, Chris, Tom, Ben, Dave, Alex, etc. In contrast, thefield Email can have a higher rarity score because email addresses aregenerally unique to individuals. In some embodiments, the system maydetermine certain emails that may not be personal to an individual butrather belong to a group and the system can discount the influence thoseemails that belong to a group In some embodiments, info@example.com orhelp@example.com may be indicative of an email address that does notbelong to an individual node profile. In this way, the node graphgeneration system 200 can assign a first rarity score to the first namefield, a second rarity score greater than the first rarity score to thelast name field, and a third rarity score greater than the second valueto the phone number field. Responsive to the match score satisfying thematch score threshold, the node graph generation system 200 can link theelectronic activity to the node profile. For example, the node graphgeneration system 200 can maintain an association in a data structure,the association indicating that the electronic activity is linked to thenode profile.

Linking an electronic activity to a node profile includes adding anentry to each value data structure of each value of each field of thenode profile that is supported by the electronic activity. As anexample, let's say the electronic activity is matched to a first nodeprofile corresponding to John Smith corresponding to a sender of theelectronic activity and a second node profile Abigail Xu correspondingto a recipient of the electronic activity. The system can identify eachof the values of the fields of the sender's node profile that issupported by the electronic activity, such as the first name of thesender, the last name of the sender, the company of the sender, theemail address of the sender and other fields that include values thatcan be supported by the signature of the sender included in the email.The system can then update the value data structure of each of thosevalues by adding an entry identifying the electronic activity as a datapoint. As such, the electronic activity can serve as a data point formultiple values of multiple fields of a particular node profile.Similarly, the system can identify each of the values of the recipient'snode profile that is supported by the electronic activity and can addentries in respective value data structures of values of fields of therecipient's node profile that are supported by the electronic activity.In this way, the electronic activity not only can update multiple valuedata structures of a single node profile but can also update the valuedata structures of multiple node profiles thereby multiplying the impacta single electronic activity can have towards the accuracy and state ofthe node profiles and the node graph in aggregate.

Referring now to FIG. 22, FIG. 22 illustrates a process flow 2200 inwhich the node graph generation system (NGGS) 200 can use relationshipinformation among multiple electronic activities to more accuratelyidentify the subset of node profiles to which to link the electronicactivities. For example, the node graph generation system 200 canidentify a sender of a first electronic activity, such as an email, anda recipient of the first electronic activity, and determine that asubsequent, second electronic activity sent by the recipient to thesender is a reply to the first electronic activity. Based on thisrelationship and based on information extracted from the secondelectronic activity used to identify a second subset of node profilesthat the second electronic activity is to be linked to, such as nodeprofiles that may potentially represent the recipient, the node graphgeneration system 200 can effectively increase a match score of linkingthe first electronic activity to node profiles of the second subset(which may not necessarily have been identified as node profiles of therecipient based only on information extracted from the first electronicactivity). The node graph generation system 200 can execute the process2200 in real-time by searching for and identifying such relationshipsresponsive to ingesting each second electronic activity, and thenupdating the corresponding match scores of the first electronic activitybased on the links made from the second electronic activity. The nodegraph generation system 200 can execute the process 2200 periodicallyand/or in near-real time, such as in a batch processing of electronicactivities.

As illustrated in FIG. 22, the node graph generation system 200 canidentify a first electronic activity 2204. The node graph generationsystem 200 can extract a first sender 2212 of the first electronicactivity 2204, and determine the first sender to be John Smith (e.g., tohave first name John and last name Smith). The node graph generationsystem 200 can extract a first recipient 2208 of the first electronicactivity 2204, and determine the first recipient to be John Doe (e.g.,to have first name John and last name Doe). For example, the node graphgeneration system 200 can extract, from the signature block 2216 of thefirst electronic activity 2204, FirstName and LastName strings, anddetermine the FirstName and LastName string to correspond to the sender2212 based on the FirstName and LastName strings being extracted fromthe signature block 2216.

The node graph generation system 200 can execute various processesdescribed herein to identify a subset of a plurality of node profilesthat match the first electronic activity 2204. For example, the nodegraph generation system 200 can identify a subset of the plurality ofnode profiles that includes a first sender subset 2220 a-2220 n, and afirst recipient subset 2224 a-2224 m. The node graph generation system200 can respectively assign sender and recipient statuses to the firstelectronic activity 2204 when identifying the node profiles of thesubsets 2220 a-2220 n, 2224 a-2224 m. The node graph generation system200 can identify the first sender subset 2220 a-2220 n by determiningthat match scores of comparing the electronic activity 2204 to the firstsender subset 2220 a-2220 n satisfy the match score threshold, anddetermining that match scores of comparing the first electronic activity2204 to the first recipient subset 2224 a-2224 m satisfy the match scorethreshold. There may be node profiles for which the comparison resultsin match scores that do not satisfy the match score threshold, such asif data extracted from the first electronic activity 2204 does not matchdata of such node profiles, even if such node profiles should match thefirst electronic activity 22 b 04.

The node graph generation system 200 can identify a second electronicactivity 2228. The node graph generation system 200 can extract a secondrecipient 2232 of the second electronic activity 2228, and determine thesecond recipient 2232 to be John Smith. The node graph generation system200 can extract a second sender 2236 of the second electronic activity2228, and determine the second sender 2236 to be John Doe. The nodegraph generation system 200 can extract, from the signature block 2240,first name, last name, and office (or cell) phone number information.The node graph generation system 200 can process the second electronicactivity 2228 to identify a subset of node profiles that match thesecond electronic activity 2228, including a second sender subset 2244a-m and a second recipient subset 2248 a-m.

The node graph generation system 200 can determine that the secondelectronic activity 2228 is a reply to or a forward of the firstelectronic activity 2204. For example, the node graph generation system200 can process metadata of the second electronic activity 2228 toidentify a status indicator indicating that the second electronicactivity 2228 is a reply to or a forward of the first electronicactivity 2204. The node graph generation system 200 can parse a subjectline of the second electronic activity 2228 to determine that the secondelectronic activity 2228 is a reply to or a forward of the firstelectronic activity 2204, such as if the subject line of the secondelectronic activity 2228 includes a string of the subject line of thefirst electronic activity 2204 that has been appended to the characters“RE:” or “FW” (in various type cases of those characters).

Responsive to determining that the second electronic activity 2228 is areply to the first electronic activity 2204, the node graph generationsystem 200 can determine at least one of (i) the recipient of the secondelectronic activity 2228 is the sender of the first electronic activity2204 or (ii) the sender of the second electronic activity 2228 is therecipient of the first electronic activity 2204. Based on thesedeterminations, the node graph generation system 200 can update thefirst recipient subset 2224 a-m to include at least one node profile ofthe second sender subset 2244 a-m that did not satisfy the match scorethreshold when the node graph generation system 200 initially identifiedthe first recipient subset 2224 a-m. For example, when identifying thesecond sender subset 2244 a-m, the node graph generation system 200 mayhave identified at least one node profile that satisfied the match scorethreshold because of the phone number extracted from the signature block2240. The node graph generation system 200 can update the first sendersubset 2220 a-n to include at least one node profile of the secondrecipient subset 2248 a-m. By using the send and reply relationships ofelectronic activities 2204 and 2228, the node graph generation system200 can more precisely identify the subsets 2220 a-n, 2224 a-m to whichto link the first electronic activity 2204, and more precisely identifythe subsets 2244 a-m and 2248 a-m to which to link the second electronicactivity 2228.

Referring now to FIG. 23, FIG. 23 illustrates a method 2300 of linkingelectronic activities to node profiles. The method 2300 can includeaccessing a plurality of electronic activities (BLOCK 2302). Withreference to FIGS. 16-18, among others, the data processing system 9300can access a plurality of electronic activities. The electronicactivities can be transmitted to the data processing system 9300 fromdata source providers. The data processing system 9300 can retrieve theelectronic activities from the data source providers. For example, thedata source provider can include or be an email server. The dataprocessing system 9300 can have the authority to access the emailsstored on the email server through an API or an HTTP method (e.g., a GETmethod). The plurality of electronic activities can be received viaelectronic accounts associated with a plurality of data sourceproviders.

The data processing system 9300 maintains a plurality of node profiles.Each node profile can include information such as first name, last name,company, and job title, each of which are represented by fields havingone or more values, each value having a confidence score assigned to thevalue. The data processing system 9300 is configured to update theplurality of node profiles using the plurality of electronic activities.

The method 2300 can include identifying a plurality of strings from dataincluded in an electronic activity of the plurality of electronicactivities, such as to link the electronic activity to one or more nodeprofiles (BLOCK 2304). For example, the data processing system 9300 canexecute the electronic activity parser 210 to identify the plurality ofstrings. The plurality of strings can correspond to fields including afirst name field, a last name field, an email address field, a phonenumber field, a title field, and a company field.

The method 2300 can include generating a plurality of activityfield-value pairs from the plurality of strings (BLOCK 2306). Forexample, the data processing system 9300 can use an electronic activityparsing policy to generate the plurality of activity field-value pairs.Each activity field-value pair can include a data structure thatassociates a value extracted from a string of the plurality of stringsto a particular field represented by the electronic activity. Forexample, the data processing system 9300 can identify a value of a firstname from the electronic activity (e.g., “John”), and associate theidentified value to a first name field, to generate an activityfield-value pair such as First Name:John. The data processing system9300 can use the electronic activity parser 210 to execute theelectronic activity parsing policy, such as to identify strings frommetadata as well as non-metadata of the electronic activity. Forexample, the data processing system 9300 can identify a first stringfrom a portion of the electronic activity, and determine a confidencescore that the first string is a first name by at least one of (i)comparing the first string to a plurality of values of a first namefield of the plurality of node profiles or (ii) a portion of theelectronic activity from which the first string was identified.

The method 2300 can include comparing the plurality of activityfield-value pairs to respective node field-value pairs of one or morenode profiles (BLOCK 2308). For example, the data processing system 9300can compare each activity field-value pair to respective field-valuepairs of each of the one or more node profiles to identify a subset ofactivity field-value pairs that match respective node field-value pairsof the one or more node profiles. The data processing system 9300 canidentify a field of the activity field-value pair, retrieve the valueassociated to the identified field, identify a corresponding field ofthe node field-value pairs, and compare the value retrieved from theactivity field-value pair to each value associated to the correspondingfield each node field-value pair. The data processing system 9300 canidentify a string type of the string corresponds to a field type of theactivity field or the node field in order to retrieve the values thatare compared.

The method 2300 can include generating a match score of the node profileindicating a likelihood that the electronic activity is transmitted orreceived by an account corresponding to the node profile based on thecomparison (BLOCK 2310). The data processing system 9300 can generatethe match scores using the comparisons of respective values of activityfield-value pairs and node field-value pairs. For example, the dataprocessing system 9300 can compare characters of the value (e.g., of thestring from which the value is extracted) to values of the correspondingfield of the activity-field value pairs. In some embodiments, the dataprocessing system 9300 determines a weighted average of a plurality ofmatch scores for a plurality of values of the electronic activity (e.g.,each value of each activity field-value pair). The data processingsystem 9300 can determine the weighted average by assigning a uniquenessscore as a weight to each value used to determine the weighted average.For example, the data processing system 9300 can assign a uniquenessscore based on the field to which the value is associated (e.g., a valueof a first name field has a lesser uniqueness score than a value of alast name field, which has a lesser uniqueness score than a value of aphone number field). The data processing system 9300 can assign auniqueness score based on a rarity of the value (e.g., some first namesmay be more rare than other first names); the data processing system9300 can assign the uniqueness score of each value based on how manynode profiles include the same value for the given field relative to thetotal number of node profiles.

The method 2300 can include determining a subset of the plurality ofnode profiles to which to link the electronic activity, responsive todetermining that the match score of each node profile of the subset ofthe plurality of node profiles satisfies a threshold (BLOCK 2312). Forexample, the data processing system 9300 can compare each match score ofeach electronic activity (which may be a weighted average) to thethreshold, and select the subset of the plurality of node profiles forwhich the comparison satisfies the threshold.

The method 2300 can include updating a data structure to include anassociation between the electronic activity and each node profile of thesubset of the plurality of node profiles (BLOCK 2314). For example, thedata processing system 9300 can generate a data structure that includesa link indicating a connection between the electronic activity and thenode profiles of the subset of the plurality of node profiles. In someembodiments, the data processing system 9300 adds entries to the nodeprofiles of the subset of the plurality of node profiles to identify theelectronic activity responsive to determining the electronic to matchthe node profiles. For example, the data processing system 9300 can addan entry to a value data structure of the value of the field of the nodeprofile that is used to match the electronic activity to the nodeprofile. For example, the data processing system 9300 can determine thatthe string used to match the electronic activity to the node profile isa string of a first name, such as the string having the value “John,”and in response can add an entry to a value data structure that includesthe value John assigned to the first name field of the node profile toidentify the electronic activity. The data processing system 9300 candetermine that a second string used to match the electronic activity isa string of a second field type, such as a last name field typecorresponding to the string having the value “Smith,” and in responsecan add a second entry to a value data structure that includes the valueSmith assigned to the last name field of the node profile to identifythe electronic activity. In some embodiments, the node profile may nothave an existing value data structure corresponding to each valueretrieved from the electronic activity. For example, the data processingsystem 9300 can determine that the node profile has a value datastructure that matches the value John for the first name field, but doesnot have a value data structure that matches the value Smith (retrievedfrom the same electronic activity as the value John) for the last namefield, and the data processing system 9300 can generate a value datastructure that includes the value Smith for the last name field.

In some embodiments, the data processing system 9300 determines acontribution score of the entry that is added to identify the electronicactivity. The data processing system 9300 can determine the contributionscore based on a trust score of a source of the electronic activity. Thecontribution score can be indicative of the data point's contributiontowards the confidence score of the value. The contribution score of adata point can decay over time as the data point becomes staler. Forexample, the contribution score can be based on a time at which the datapoint (e.g., the value) was generated or last updated. The dataprocessing system 9300 can use the contribution score to determine aconfidence score of the value. The data processing system 9300 can useeach contribution score associated with each entry that indicates thevalue to calculate the confidence score of the value. For example, forthe value “John” of a first name field, the data processing system 9300can determine a weighted average of contribution scores of eachelectronic activity from which the value “John” is identified todetermine the confidence score. By linking electronic activities to nodeprofiles, and using contribution scores to determine the confidencescore of each value, the data processing system 9300 can use theelectronic activities as data points that support the value associatedwith the field, such as to enable an objective an accurate indication ofthe value that should correspond to the electronic account that the nodeprofile represents.

In some embodiments, the method 2300 includes selecting a first nodeprofile of the subset of the plurality of node profiles based on thematch score of the first node profile, and linking the electronicactivity with the first node profile. For example, the data processingsystem 9300 can rank each node profile of the subset of the plurality ofnode profiles based on each respective match score, and select the firstnode profile as the node profile having a greatest match score in orderto link the node profile having the greatest match score to theelectronic activity.

18. Linking Record Objects to Node Profiles

The present solution can enable real-time or near real-time linking ofrecord objects to node profiles, with increased accuracy. In somesystems that maintain data regarding entities, such as individuals orenterprises, including systems of record, the data may be self-reported,such as in response to specific queries to provide data for fields suchas first name, last name, title, or email. As such, this data may beinaccurate. For example, when the data was provided, the data may havebeen inaccurate due to the data being self-reported. At a particularinstant in time after the data was provided, due to changes to the datathat may have occurred subsequent to when the data was provided andbefore the data has been updated, the data even if it was previouslycorrect at the time the data was provided, may also eventually becomeobsolete, stale or inaccurate.

The present solution described herein can match record objects ofsystems of record to node profiles maintained by a node graph generationsystem, that can use the data included in the record objects to updatenode profiles and the values of fields of these node profilesunobtrusively and without requiring any direct human input. As such, thepresent disclosure describes solutions for maintaining node profilesthat remain accurate as the node profiles do not rely directly onself-reported information submitted by a user to update the node profileand because the node profiles are automatically updated as recordobjects are ingested and processed by the system without requiring anyhuman activity. In this way, the present solution can enable dynamicupdates to node profiles and a node graph including such node profiles,rather than manual/self-reported updates. Even if the underlying recordobjects include self-reported information, the present solution canmaintain contribution scores of each data source of record objects,update the contribution scores based on verification of the dataextracted from the data source, and determine confidence scores of eachvalue of a field of the node profile based on the contribution scores ofthe record objects that support that value.

By linking record objects to node profiles, the present solution canincrease the accuracy and validity of the node profiles, such as byincreasing a likelihood that each node profile represents the true stateof the world. For example, when node profiles are used to generate anode graph indicative of a hierarchy or other relationships amongst nodeprofiles, the present solution can more accurately represent values offields such as job titles that are used to generate the node graph. Thepresent solution can more accurately rank each value of each field (eachvalue representing a potential true state of the world) by dynamicallyupdating the confidence score corresponding to each value responsive toextracting data from record objects, so that the present solutionoutputs an evidence-based estimation of which value is the true valuewith improved accuracy. As an example, a node profile can include afirst email address corresponding to a first job and a second emailcorresponding to a subsequent job. Each of the two email addresses areat respective points in time, accurate and valid. As the person switchesjobs, the first email address is no longer valid but the confidencescore associated with the email address can in some embodiments, remainhigh indicating that the first email address belongs to the nodeprofile. Similarly, the second email address also belongs to the nodeprofile and therefore also has a high confidence score. After the systemdetermines that the second email address is active and functioning, thesystem can assign a higher confidence score to the second email addressrelative to the first email address since the contribution scoresprovided by recent data points (for example, recent record objectsidentifying the second email address) can contribute towards the higherconfidence score. The present solution can thus respond to changes inthe true state of the world represented by the node profile using thesecond email, rather than relying on self-reported information which maybe inaccurate and/or delayed.

Referring further to FIG. 2, among others, the node graph generationsystem 200 can ingest record objects to generate or update node profilesthat are maintained by the node graph generation system 200 using datafrom the record objects. For example, as illustrated in FIG. 10, thenode graph generation system 200 can process record objects or datarecords of a system of record, such as a customer relationshipmanagement (CRM) system. The node graph generation system 200 canprocess record objects of systems of records such as Applicant TrackingSystems (ATS), such as Lever, located in San Francisco, Calif. or Talendby Talend Inc., located in Redwood City, Calif., enterprise resourceplanning (ERP) systems, customer success systems, such as Gainsightlocated in Redwood City, Calif., Document Management Systems, amongothers. As illustrated in FIG. 10, the record objects can include a leadrecord object 1000, an account record object 1002, an opportunity recordobject 1004, or a contact record object 1006.

The node graph generation system 200 can process the record objects toidentify a plurality of object fields of the record objects. Forexample, each record object can have one or more object field-valuepairs. The node graph generation system 200 can process the recordobject to identify values from structured data fields of the recordobjects. In some embodiments, the node graph generation system 200 canexecute a record object parsing policy to identify values fromunstructured data of the record objects, such as by executing functionsof the record data extractor 230. The node graph generation system 200can generate a plurality of object field-value pairs that associate theidentified values to the corresponding fields. Because each recordobject may include multiple strings having data that corresponds to aparticular field, the node graph generation system 200 can generatemultiple object field-value pairs from each electronic activity (e.g.,multiple first name-value field pairs based on multiple record entriesof the record object).

Referring further to FIG. 6A, the node graph generation system 200 canmaintain a plurality of node profiles 600. Each node profile 600includes a plurality of node field-value pairs corresponding toattributes 610 and value data structures 615. For example, the nodegraph generation system 200 can maintain a first node field-value pairassociating a first value 620 (e.g., Va) to field 610(1), a second nodefield-value pair associating a second value 620 (e.g., Vb) to the field610(1), and so on for each value. As shown for the node profileillustrated in the table above, the node graph generation system 200 cangenerate a first field-value pair associating a value of John to thefirst name field, and a second field-value pair associating a value ofJohnathan to the first name field.

The node graph generation system 200 can compare the object field-valuepairs of a record object to be matched to respective node field-valuepairs of one or more candidate node profiles with which to match therecord object. The node graph generation system 200 can compare one ormore object field-value pairs of the record object to corresponding nodefield-value pairs of a candidate node profile to determine a match scorebetween the record object and the candidate node profile. The node graphgeneration system 200 can identify a node profile with which to matchthe record object based on the match score. Node profiles having a matchscore below a predetermined threshold can be determined not to bematched.

To compute the match score, the node graph generation system 200 caniterate through each object field-value pair, identify the field of thenode field-value pair, and identify a corresponding field of a nodefield-value pair of the node profile. For example, the node graphgeneration system 200 can identify the field of the object field-valuepair to be first name, and based on the identification, select the fieldof the node field-value pairs that will be used for the comparison to bethe first name field of the node field-value pairs. The node graphgeneration system 200 can retrieve the value from the object field-valuepair, retrieve a corresponding value that is associated to theidentified field of the node field-value pair, and compare the values.For example, the node graph generation system 200 can select the firstname field of a first object field-value pair, identify a correspondingfirst name field of a first node field-value pair, retrieve the value ofthe first name from the first object field-value pair, retrieve thecorresponding value of the first name from the first node field-valuepair, and compare the retrieved values.

With reference to the account record object 1002 of FIG. 10 and nodeprofile NPID-12 of FIG. 6B, the node graph generation system cangenerate an object field-value pair of Field 1:XYZ (e.g., “John”),identify the field to be first name, identify the corresponding firstname field of each node field-value pair of the node profile NPID-12,retrieve the first name John from the object field-value pair, andretrieve the first name John from the node field-value pair (or thefirst name Johnathan from the second value that is assigned to the firstname field of the node profile NPID-12). The node graph generationsystem 200 can compare the first name John of the object field-valuepair to the first name John of the node field-value pair, and calculatea match score based on the comparison. The node graph generation systemcan then match object field value pairs of the record object withremaining field-value pairs of the node profile and based on thecomparison of these object field value pairs and field-value pairs,determine a match score between the record object and the node profilebased on a number of pairs that matched. In some embodiments, the matchscore can be based on which node field-value pairs matched. Forinstance, node field-value pairs that are more unique to node profilesin the node graph generation system can contribute more to the matchscore than node field-value pairs that are less unique. For example, thefield-value pair for a very large company, such as Google may not be asunique as a cell phone number of a particular person. Moreover, nodefield-value pairs that have values with a higher confidence score cancontribute more to the match score than field-value pairs that havevalues with a lower confidence score to improve the accuracy of linkingor matching record objects to node profiles. The node graph generationsystem 200 can calculate match scores for each comparison of the recordobject and respective candidate node profiles.

The node graph generation system 200 can compare each match scorebetween the record object and the node profile to a match scorethreshold to determine whether the record object is to be matched to thenode profile. The node graph generation system 200 can calculate anaverage (e.g., weighted average) of each match score determined for eachcomparison for the record object, and compare the weighted average tothe match score threshold to determine whether the record object matchesthe node profile.

The node graph generation system 200 can apply various rules todetermine how to calculate the weighted average. In some embodiments,the node graph generation system 200 calculates the weighted averagebased on a measure of uniqueness of the field of the value used tocalculate the match score. The node graph generation system 200 canapply different weights to different fields based on the rarity score ofthe field. The rarity score of the field can be determined by generatinga count of each value of the field across all node profiles maintainedby the node graph generation system. If a predetermined number orthreshold of values have a frequency count that satisfies apredetermined threshold, the field can have a lower rarity score thananother field in which none of the values have a frequency count thatexceeds the predetermined threshold. For example, the field FirstNamecan have a low rarity score because there are a lot of common firstnames, such as John, Chris, Tom, Ben, Dave, Alex, etc. In contrast, thefield Email can have a higher rarity score because email addresses aregenerally unique to individuals. In some embodiments, the system maydetermine certain emails that may not be personal to an individual butrather belong to a group and the system can discount the influence thoseemails that belong to a group In some embodiments, info@example.com orhelp@example.com may be indicative of an email address that does notbelong to an individual node profile. In this way, the node graphgeneration system 200 can assign a first rarity score to the first namefield, a second rarity score greater than the first rarity score to thelast name field, and a third rarity score greater than the second valueto the phone number field.

In some embodiments, the node graph generation system 200 calculates theweighted average based on a measure of uniqueness of the value inaddition to or in contrast to the rarity score of the field. Forexample, certain names may be more unique (e.g., rare) than other names.The node graph generation system 200 can maintain a uniqueness datastructure mapping each value of each field of each of the plurality ofnode profiles to a corresponding uniqueness or frequency count, andretrieve the uniqueness using the value. The node graph generationsystem 200 can generate the uniqueness data structure using theplurality of node profiles, and update the uniqueness data structureresponsive to receiving node profile data. For example, the node graphgeneration system 200 can count a number of each unique value of thenode profiles, and calculate the uniqueness for each unique value basedon the count. The node graph generation system 200 can thus rely moreheavily on data extracted from the record object that has a higherlikelihood of specifically corresponding to the node profile (e.g.,rather than matching the record object from which the first name valueof John was extracted to every node profile having the first name John).

Responsive to the match score satisfying the match score threshold, thenode graph generation system 200 can link the electronic activity to thenode profile. For example, the node graph generation system 200 canmaintain an association in a data structure. The association canindicate that the electronic activity is linked to the node profile. Thenode graph generation system 200 can update a confidence score of eachvalue of the node profile that matches corresponding value(s) extractedfrom the electronic activity.

Referring further to FIG. 10, the node graph generation system 200 canuse relationship information amongst record objects to more preciselydetermine the subset of node profiles that match the record objects. Forexample, the node graph generation system 200 can identify that tworecord objects are linked based on data such as opportunity contact role(OCR) objects, a conversion of a lead record object 100 into a contactrecord object 1006, an account record object 1002, and an opportunityrecord object 1004, or other links between record objects, includingexplicit or implicit linking of record objects. Responsive toidentifying a link between two record objects, the node graph generationsystem 200 can use the node profiles that are determined to match one ofthe record objects to update the matches to the other record object. Forexample, responsive to determining that the lead record object 1000 islinked to the account record object 1002, the node graph generationsystem 200 can increase a match score between the lead record object andnode profiles that are matched to the account record object 1002. Thiscan be useful in the event where a few node profiles have a high matchscore with a first node profile but there are many node profiles thatare candidate matches to a second record object linked to the firstrecord object. In such an event, the system can identify the nodeprofiles linked to the first record object to identify the same nodeprofiles from the many node profiles that are candidate matches to thesecond record object.

For example, the node graph generation system 200 can identify the leadrecord object 1000, and determine a first subset of node profiles thatmatch the lead record object as described herein, such as by comparingobject field-value pairs of the lead record object to node field-valuepairs of the node profiles of the first subset and evaluating matchscores of the comparison relating to a match score threshold. The nodegraph generation system 200 can determine that the lead record object1000 is linked to the account record object 1002 from informationincluded in the record objects or the system of record. The node graphgeneration system 200 can determine a second subset of node profilesthat match the account record object 1002 as described herein, such asby comparing object field-value pairs of the account record object 1002to node field-value pairs of the node profiles and evaluating matchscores of the comparison relative to a match score threshold. In someembodiments, responsive to determining that the account record object1002 is linked to the lead record object 1000, the node graph generationsystem 200 can add the node profiles of the second subset to the nodeprofiles of the first subset. In some embodiments, responsive todetermining that the account record object 1002 is linked to the leadrecord object 1000, the node graph generation system 200 increases amatch score of the comparison of the node field-value pairs of the nodeprofiles of the second subset to the object field-value pairs of thelead record object 1000. As such, even if certain record objects haveincomplete information that may result in inaccurately low match scores,by using the linking between record objects, the node graph generationsystem 200 can more accurately identify the node profiles that match therecord objects (and thus the node profiles to which to link the recordobjects). This improves the accuracy of the matches made between recordobjects and node profiles and further improves the accuracy of thevalues of the fields of the node profile, thereby improving the accuracyof the node graph and the insights and analytics derived from the nodeprofiles and the node graph.

Referring now to FIG. 24, FIG. 24 illustrates a method 2400 of linkingrecord objects to node profiles. The method 2400 can include accessing aplurality of record objects of one or more systems of record (BLOCK2402). Each record object corresponds to a record object type (e.g.,lead record object, account record object, among others) and includesone or more object field-value pairs associating an object field valueto a corresponding field of the record object. The systems of recordcorrespond to one or more data source providers. The data processingsystem 9300 can retrieve the record objects from the systems of record.

The method 2400 can include maintaining a plurality of node profilescorresponding to a plurality of unique entities (BLOCK 2404). Each nodeprofile includes one or more field-value pairs associating a node fieldvalue to a corresponding field of the node profile. For example, thedata processing system 9300 can maintain a plurality of node profilesthat can include information such as first name, last name, company, andjob title, each of which are represented by fields having one or morevalues, each value having a confidence score assigned to the value. Thedata processing system 9300 is configured to update the plurality ofnode profiles using the plurality of record objects.

The method 2400 can include identifying a record object to match to atleast one node profile of the plurality of node profiles (BLOCK 2406).For example, the data processing system 9300 can parse the system ofrecord of the record object to identify the record object periodically,or responsive to detecting or receiving an indication of an update tothe system of record. The data processing system 9300 can identify fromeach record object a plurality of object field-value pairs thatassociate a value of an object field to the object field. For example,the data processing system 9300 can identify a first name field of therecord object, extract the first name from the first name field, andgenerate the object field-value pair to associate the first name to thefirst name field.

The method 2400 can include comparing the object field values of the oneor more object field-value pairs of the record object to correspondingnode field values of the corresponding fields of the node profile (BLOCK2408). For example, the data processing system 9300 can compare eachobject field-value pair to respective field-value pairs of each of theone or more node profiles to identify a subset of object field-valuepairs that match respective node field-value pairs of the one or morenode profiles. The data processing system 9300 can identify a field ofthe object field-value pair, retrieve the value associated to theidentified field, identify a corresponding field of the node field-valuepairs, and compare the value retrieved from the object field-value pairto each value associated to the corresponding field each nodefield-value pair.

The method 2400 can include generating a match score based on thecomparison that indicates a likelihood that the record objectcorresponds to the node profile (BLOCK 2410). The data processing system9300 can generate the match scores using the comparisons of respectivevalues of object field-value pairs and node field-value pairs. Forexample, the data processing system 9300 can compare characters of thevalue (e.g., of the string from which the value is extracted) to valuesof the corresponding field of the object field-value pairs. In someembodiments, the data processing system 9300 determines a weightedaverage of a plurality of match scores for a plurality of values of therecord object (e.g., each value of each object field-value pair). Thedata processing system 9300 can determine the weighted average byassigning a uniqueness score as a weight to each value used to determinethe weighted average. For example, the data processing system 9300 canassign a uniqueness score based on the field to which the value isassociated (e.g., a value of a first name field has a lesser uniquenessscore than a value of a last name field, which has a lesser uniquenessscore than a value of a phone number field). The data processing system9300 can assign a uniqueness score based on a rarity of the value (e.g.,some first names may be more rare than other first names); the dataprocessing system 9300 can assign the uniqueness score of each valuebased on how many node profiles include the same value for the givenfield relative to the total number of node profiles.

The method 2400 can include determining a subset of the plurality ofnode profiles with which to link the record object responsive todetermining that the match score of each node profile of the subsetsatisfies a threshold (BLOCK 2412). For example, the data processingsystem 9300 can compare each match score of each record object (whichmay be a weighted average) to the threshold, and select the subset ofthe plurality of node profiles for which the comparison satisfies thethreshold.

The method 2400 can include updating a first value data structure of thefirst node field value by adding an entry identifying the record object(BLOCK 2414). For example, the data processing system 9300 can generatea data structure that includes a link indicating a connection betweenthe record object and the node profiles of the subset of the pluralityof node profiles. In some embodiments, the data processing system 9300adds entries to the node profiles of the subset of the plurality of nodeprofiles to identify the record object responsive to determining therecord object to match the node profiles. For example, the dataprocessing system 9300 can add an entry to a value data structure of thevalue of the field of the node profile that is used to match the recordobject to the node profile. For example, the data processing system 9300can determine that the data used to match the record object to the nodeprofile is of a first name field, such as a string having the value“John,” and in response can add an entry identifying the record objectto a value data structure that includes the value John assigned to thefirst name field of the node profile. The data processing system 9300can determine that a second string used to match the record object is ofa second field type, such as a last name field type corresponding to thestring having the value “Smith,” and in response can add a second entryidentifying the record object to a value data structure that includesthe value Smith assigned to the last name field of the node profile. Insome embodiments, the node profile may not have an existing value datastructure corresponding to each value retrieved from the record object.For example, the data processing system 9300 can determine that the nodeprofile has a value data structure that matches the value John for thefirst name field, but does not have a value data structure that matchesthe value Smith (retrieved from the same record object as the valueJohn) for the last name field, and the data processing system 9300 cangenerate a value data structure that includes the value Smith for thelast name field.

The method 2400 can include updating a confidence score of the firstnode field value based on the entry identifying the record object (BLOCK2416). For example, the data processing system 9300 can increase theconfidence score responsive to adding the entry, as the entry willfurther support an expectation that the value is a true, accurate valuefor that field. The data processing system 9300 can update theconfidence score based on a contribution score of the entry. Thecontribution score can indicate a trustworthiness of the source of theentry, and can be updated over time by periodically comparing values ofrecord objects retrieved from particular data sources (e.g., systems ofrecord) to known values (or values having high confidence). For example,the data processing system 9300 can generate the contribution scorebased on a trust score assigned to the system of record that isassociated with the record object. In some embodiments, the dataprocessing system 9300 determines the confidence score for the firstnode field value based on the contribution scores of the entries used toprovide the values to the first node field value. For example, the dataprocessing system 9300 can determine the confidence score based on anaverage of the contribution scores. The contribution score can be basedon a time at which the record object was last updated or modifiedrelative to a time at which the contribution score is calculated, suchas to decrease the contribution score based on a difference between thetwo times.

In some embodiments, the record object includes multiple objectfield-value pairs. The data processing system 9300 can match a firstobject field value to a first node field value, and a second objectfield value to a second node field value. The data processing system9300 can generate a first confidence score for the first node fieldvalue based on the entry that identifies the record object, and cangenerate a second confidence score for the second node field value basedon the entry that identifies the record object.

In some embodiments, the data processing system 9300 maintains a shadowrecord object corresponding to the record object. Responsive to matchingthe record object to the subset of node profiles, the data processingsystem 9300 can add values from the node profile(s) of the subset to theshadow record object, which can facilitate completing or updating theshadow record object. For example, the data processing system 9300 canretrieve one or more values from the node profile(s) of the subset andadd the one or more values to one or more shadow object fields of theshadow record object. In some embodiments, the data processing system9300 provides a notification to a device to update a value of the objectfield of the record object based on the one or more values added to theone or more shadow object fields of the shadow record object. As such,the data processing system 9300 can increase a completeness of therecord object by matching the record object to the subset of nodeprofiles, and then using values from the subset of node profiles tocomplete the record object. In some embodiments, the data processingsystem 9300 identifies the values from the subset of node profiles toadd to the shadow record object and/or the record object responsive tothe confidence scores of the values satisfying a confidence scorethreshold, which can increase the accuracy of the completion of theshadow record object and/or the record object.

In some embodiments, the data processing system 9300 uses values of thesubset of node profiles to update the record object responsive tomatching the record object to the subset of node profiles, which canincrease the accuracy of the record object, and thus enable featuressuch as more accurate determination of stages associated with the recordobject. For example, the data processing system 9300 can determine thata particular object field value of a field of the record object isdifferent than a node field value of a corresponding field of the nodeprofile. In response, the data processing system 9300 can retrieve theconfidence score of the node field value, and compare the confidencescore to a predetermined threshold. Responsive to the confidence scoresatisfying the predetermined threshold, such as being greater than thepredetermined threshold, the data processing system 9300 can generate arequest to update the object field value of the record object. The dataprocessing system 9300 can generate the request to include the nodefield value that has the confidence score that satisfied thepredetermined threshold and/or cause the system of record to update theobject field value to be the node field value.

19. Generating Confidence Scores of Values of Fields Based on DataPoints

The present disclosure relates to systems and methods for generating andupdating confidence scores of values of one or more field of nodeprofiles. By generating and updating confidence scores of values, asystem can determine, at any point in time, a current state of the nodeprofile while providing a level of confidence for each value. Inexisting systems that may maintain some form of a node profile, the nodeprofile can include values that are static and only get updatedresponsive to a change made by a user. In the present disclosure,because the node profiles include value data structures that arecontinually updated by adding entries identifying new data points thatsupport the value, the system is able to dynamically update the nodeprofile without any user intervention, while at the same time, compute aconfidence score of one or more values of the node profile. This allowsa user querying the system to determine, at any given point in time, astate of the node profile, including the state of the node profile atany point in the past.

Moreover, the present disclosure can generate and update confidencescores of values of one or more fields of multiple node profiles. Insome embodiments, the present disclosure can update node profiles, ormore particularly, value data structures of node profiles as the systemingests and processes one or more electronic activities or recordobjects of systems of record. A single electronic activity or recordobject can serve as a data point for multiple value data structures ofeither a single node profile or multiple node profiles. In this way, asingle electronic activity can update multiple node profilessimultaneously, accelerating the speed at which the system can generateand update node profiles and construct the node graph based on the nodeprofiles. Based on the present disclosure, confidence scores of valuescan be generated and updated as more data points are processed, and as aresult of dynamically and automatically updating confidence scores byassigning data points to values of fields of node profiles, the systemcan maintain and update a node graph of node profiles that is updatedwithout human intervention and is more accurate than existing systems asit is dynamically updated, the source of data used to update the nodeprofiles is not centralized and not reported by any one individual oruser. Moreover, because electronic activities are constantly beinggenerated, ingested and processed, the node profiles do not remainstatic. Referring now to FIG. 25, FIG. 25 illustrates a series ofelectronic activities between two nodes. As described herein, and alsoreferring to FIGS. 6B and 8, a first node N1 and a second node N2 mayexchange a series of electronic activities. FIG. 25 also shows arepresentation of two electronic activities and representations of twonode profiles of two nodes at two different states according toembodiments of the present disclosure.

As shown in FIG. 25, a first electronic activity sent at a first time,T=T₁, and a second electronic activity sent at a second time, T=T₂, areshown. The first electronic activity 652 a includes or is associatedwith a first electronic activity identifier 654 a (“EA-001”). The secondelectronic activity 652 b includes or is associated with a secondelectronic activity identifier 654 b (“EA-002”). The system 200 canassign the first electronic activity identifier 654 a to the firstelectronic activity 652 a and second electronic activity identifier 654b to the second electronic activity 652 b. In some embodiments, thesystem 200 can assign the first and second electronic activities' uniqueelectronic activity identifiers to allow the system to uniquely identifyeach electronic activity processed by the system 200. Collectively, thefirst and second electronic activities can be referred to herein aselectronic activities 652 or individually as electronic activity 652.Each electronic activity can include corresponding metadata, asdescribed above, a body, and a respective signature 660 a and 660 bincluded in the body of the respective electronic activity 652.

The second electronic activity can be sent as a response to the firstelectronic activity. The system 200 can determine that the secondelectronic activity is a response to the first electronic activity usingone or more response detection techniques based on signals included inthe electronic activity including the metadata of the electronicactivity, the subject line of the electronic activity, the participantsof the electronic activity, and the body of the electronic activity. Forinstance, the system can determine that the second electronic activityhas a timestamp after the first electronic activity. The system 200 candetermine that the second electronic activity identifies the sender ofthe first electronic activity 652 a as a recipient of the secondelectronic activity 652 b. The system can determine that the secondelectronic activity includes a subject line that matches one or morewords of the subject line of the first electronic activity. In someembodiments, the system can determine that the second electronicactivity includes a subject line that includes the entire string ofcharacters of the subject line of the first electronic activity and thestring of characters is preceded by “RE:” or some other predeterminedset of characters indicating that the second electronic activity is areply. In some embodiments, the system can determine that the body ofthe second electronic activity includes the body of the first electronicactivity. The system 200 can also determine that the second electronicactivity is a response to the first electronic activity based on theparticipants included in both the electronic activities. Furthermore, insome embodiments, the system 200 can determine if the second electronicactivity is a forward of the first electronic activity or a reply all ofthe first electronic activity.

FIG. 25 also includes two representations of two node profilesassociated with the first node N1 and the second node N2 at twodifferent times, T=T₁ and T=T₂. The node profile NPID-1 corresponds to afirst node profile of the first node N1, who is the sender of theelectronic activities 652 a. The first representation 662 a 1 of thefirst node profile was updated after the first electronic activity 652 awas ingested by the node graph generation system 200 but before thesecond electronic activity 652 b was ingested by the system 200. Thesecond representation 662 b 1 of the first node profile was updatedafter the first and second electronic activities 652 a and 652 b wereingested by the node graph generation system 200.

The node profile NPID-2 corresponds to a second node profile of one ofthe recipients of the electronic activity 652 a and the sender of thesecond electronic activity 652 b. The first representation 662 a 2 ofthe second node profile was updated after the first electronic activity652 a was ingested by the node graph generation system 200 but beforethe second electronic activity 652 b was ingested by the system 200. Thesecond representation 662 b 2 of the second node profile was updatedafter the first and second electronic activities 652 a and 652 b wereingested by the node graph generation system 200.

In some embodiments, as described herein, the node profile manager 220of the system 200 can maintain, for each value of each field of eachnode profile, a value data structure that can be stored as amultidimensional array. The multidimensional array can include a list ofentries identifying data points that identify electronic activities orsystem of records that contribute to the value of the field. Each datapoint can be associated with a source. For emails or other electronicactivities, the source can be a mail server of a data source provider.For record objects, the source of the record object can be a system ofrecord of the data source provider. Each source of a respective datapoint can have an associated trust score that can be used to determinehow much weight to assign to the data point from that source. Each datapoint can also identify a time at which the data point was generated(for instance, in the case of a data point derived from an electronicactivity such as an email, the time the data point was generated can bethe time the electronic activity was sent or received). In the case of adata point being derived from a system of record, the time the datapoint was generated can be the time the data point can be entered intothe system of record or the time the data point was last accessed,modified, confirmed, or otherwise validated in or by the system ofrecord. The source of the data point and the time the data point wasgenerated, last accessed, updated or modified, can be used to determinea contribution score of the data point, which can be used to determinethe confidence score of the value. In some embodiments, the node profilemanager 220 can generate, compute or assign a contribution score to eachdata point. The contribution score can be indicative of the data point'scontribution towards the confidence score of the value. The contributionscore of a data point can decay over time as the data point becomesstaler. The contribution scores of each of the data points derived fromelectronic activities and systems of record can be used to compute theconfidence score of the value of a field of the node profile.

Each of the representations 662 of the first and second node profilescan include fields and corresponding values. For example, in the firstrepresentation 662 a 1, the field “First Name” is associated with thevalue John. The first representation 662 a 1 of the first node profilealso includes the field “Title” which is associated with the value“Director.” The values of the last name and cell phone number remain thesame in both the representations 662 a 1 and 662 b 1 of the first nodeprofile. In another example, in the first representation 662 a 2 of thesecond node profile, the field “First Name” is associated with the valueAbigail. The first representation 662 a 2 of the second node profiledoes not include the field “Title” as that information may not have beenavailable to the system 200. It should be appreciated that in the eventthe value was already associated with the field, the system 200 canupdate the value data structure of the value by adding an entryidentifying the electronic activity. In this way, the electronicactivity serves as a data point that supports the value and can increasethe confidence score of the value, which can further improve theaccuracy of the information included in the node profile.

In the representation 662 b 2 of the second node profile NPID-2, thesecond node profile was updated after the first and second electronicactivities 652 a and 652 b were ingested. The field “Title” is nowassociated with the value “Manager.” The values of the “Work Phone No”and “Cell Phone No” fields have new values associated with them. In therepresentation 662 b 1 of the first node profile NPID-1, the first nodeprofile was updated after the first and second electronic activities 652a and 652 b were ingested. The field “First Name” is now associated with2 different values, John and Johnathan. In the representative nodeprofiles of NPID-1 and NPID-2, the same electronic activity can updatedifferent node profiles.

It should be appreciated that the value data structure of the valueJ@acme.com corresponding to the email field of the first node profilecan be updated to include an entry identifying the second electronicactivity 652 b. It should further be appreciated that the system 200 isconfigured to updated the field-value pair of the first node profilecorresponding to email: J@acme.com, even though J@acme.com is a valuepreviously associated with the email field of the first node profile.The system can use the second electronic activity to update the nodeprofile by not only adding new values, such as the name “Johnathan” butalso by updating the value data structures of existing values of thefirst node profile to include entries identifying the second electronicactivity 654 b. By doing so, the system 200 can continuously maintainthe accuracy of the data included in the node profiles and identifywhich values are still current and which values are now stale based onthe last time a data point supported the particular value. As describedherein, the system 200 can be configured to generate respectivecontribution scores to each entry included in the value data structureof a value and use the respective contribution scores of each entry ofthe value data structure to determine a confidence score of the value ofthe field of the node profile. The system can further be configured todynamically update the contribution scores and the confidence scorebased on a current time as the contribution scores of data points canchange with time. In some embodiments, the contribution scores of datapoints can decrease with time as the data point becomes older.

Referring now to FIG. 26, FIG. 26 illustrates a representation of a nodeprofile 2600 of a node. As described herein, and also referring to FIG.6A, the node profile can include one or more fields associated with oneor more values. Each value can include a corresponding value datastructure. The value data structure can include one or more entries.Each entry of the value data structure can identify a data point 2602representing an electronic activity or a record object. In someembodiments, the node profile manager 220 can generate and assign acontribution score 2610 to each data point 2602 for the value to whichthe data point serves as evidence. The contribution score 2610 can beindicative of the data point's contribution towards the confidence score2614 of the value. The contribution score 2610 of a data point 2602 candecay over time as the data point 2602 becomes staler. The contributionscores 2610 of each of the data points 2602 derived from electronicactivities and systems of record can be used to compute the confidencescore 2614 of the value of a field of the node profile.

As described herein, each of the values included in the node profile canbe supported by one or more data points 2602. Data points 2602 can bepieces of information or evidence that can be used to support theexistence of values of fields of node profiles. A data point 2602 can bean electronic activity, a record object of a system of record or otherinformation that is accessible and processable by the system 200. Insome embodiments, a data point 2602 can identify an electronic activity,a record object of a system of record, or other information that isaccessible and processable by the system 200 that serves as a basis forsupporting a value in a node profile. Each data point 2602 can beassigned its own unique identifier. Each data point 2602 can beassociated with a source of the data point 2602 identifying an origin ofthe data point 2602. The source of the data point 2602 can be a mailserver, a system of record, among others. Each of these data points 2602can also include a timestamp. The timestamp of a data point 2602 canidentify when the data point 2602 was either generated (in the case ofan electronic activity such as an email) or the record object thatserves as a source of the data point 2602 was last updated (in the casewhen the data point 2602 is extracted from a system of record). Eachdata point 2602 can further be associated with a trust score of thesource of the data point 2602. The trust score of the source can be usedto indicate how trustworthy or reliable the data point 2602 is. The datapoint 2602 can also be associated with a contribution score that canindicate how much the data point 2602 contributes towards a confidencescore 2614 of the value associated with the data point 2602. Thecontribution score 2610 can be based on the trust score of the source(which is based in part on a health score of the source) and a time atwhich the data point 2602 was generated or last updated.

In some embodiments, a confidence score 2614 of the value can indicate alevel of certainty that the value of the field is a current value of thefield. The higher the confidence score 2614, the more certain the valueof the field is the current value. The confidence score 2614 can bebased on the contribution scores 2610 of individual data points 2602associated with the value. The confidence score 2614 of the value canalso depend on the corresponding contribution scores 2610 of othervalues of the field, or the contribution scores of data points 2602associated with other values of the field.

Below is a reproduced portion of Table 1. The table illustrates variousvalues for various fields and includes an array of data points thatcontribute to the respective value. As shown in the table, the sameelectronic activity can serve as different data points for differentvalues. Further, the table illustrates a simplified form for the same ofconvenience and understanding.

Trust Contribution Data Point # DP ID TimeStamp Activity ID Source ScoreScore Field: First Name Value: John [Confidence score] = 0.8 Data Point1: DP ID101 2/1/2016 4 pm ET EA-003 Email 100 0.6 Data Point 2: DP ID2252/18/2017 2pm ET SOR-012 CRM 70 0.4 Data Point 3: DP ID343 3/1/2018 1 pmET EA-017 Email 100 0.7 Data Point 4: DP ID458 7/1/2018 3 pm ET EA-098Email 100 0.8 Data Point 5: DP ID576 9/12/2015 3 pm ET SOR-145 Talend 200.2 Field: First Name Value: Johnathan [Confidence score] = 0.78 DataPoint 1: DP ID101 2/1/2016 4 pm ET EA-003 Email 100 0.6 Data Point 2: DPID225 2/18/2017 2pm ET SOR-012 CRM 70 0.4 Data Point 3: DP ID3433/1/2018 1 pm ET EA-017 Email 100 0.7 Data Point 4: DP ID458 7/1/2018 3pm ET EA-098 Email 100 0.8 Data Point 5: DP ID576 9/12/2015 3 pm ETSOR-145 Talend 20 0.2 Field: Title Value: Director [Confidence score] =0.5 Data Point 1: DP ID101 2/1/2016 4 pm ET EA-003 Email 100 0.6 DataPoint 2: DP ID225 2/18/2017 2pm ET SOR-012 CRM 70 0.4 Data Point 3: DPID243 3/1/2017 1 pm ET EA-117 Email 100 0.65 Data Point 4: DP ID2433/1/2018 1 pm ET SOR-087 CRM 5 0.05 Field: Title Value: CEO [Confidencescore] = 0.9 Data Point 1: DP ID343 3/1/2018 1 pm ET EA-017 Email 1000.7 Data Point 2: DP ID458 7/1/2018 3 pm ET EA-098 Email 100 0.8 DataPoint 3: DP ID225 3/18/2018 2pm ET SOR-015 CRM 65 0.54

As a result of populating values of fields of node profiles usingelectronic activities, the node profile manager 220 can generate a nodeprofile that is unobtrusively generated from electronic activities thattraverse networks. In some embodiments, the node profile manager 220 cangenerate a node profile that is unobtrusively generated from electronicactivities and systems of record.

As described herein, the present disclosure relates to methods andsystems for assigning contribution scores to each data point (forexample, electronic activity) that contributes to a value of a fieldsuch that the same electronic activity can assign different contributionscores to different values of a single node profile and of multiple nodeprofiles. The contribution score can be based on a number of differentelectronic activities contributing to a given value of a field of a nodeprofile, a recency of the electronic activity, among others. In someembodiments, a system of record of an enterprise accessible to the nodegraph generation system 200 can include data that can also contribute toa value of a field of a node profile. The contribution score can bebased on a trust score or health score of the system of record. In someembodiments, the contribution score can be based on a number ofdifferent electronic activities or systems of record contributing to thevalue of the field of the node profile. In some embodiments, thecontribution score can be based on a number of different electronicactivities or systems of record contributing to other values of thefield of the node profile. In some embodiments, the contribution scorecan be based on when the value was last updated or modified within thesystem of record, among others.

Referring back to Table 1, various factors can affect the contributionscore of a given data point 2602. For example, a high trust score of asource of the data point can promote a higher contribution score. Datapoints corresponding to electronic activities generally have a highercontribution score while data points corresponding to systems of recordwith lower trust scores can have a lower contribution score. This may bebecause systems of record generally include data that is manually inputby a user and remains static until it is modified. In contrast, the datain electronic activities, such as emails, are generated by multiplesenders and include signatures that are updated by the creator of thesignature i.e., the sender of the email. Although it is possible that anindividual user may include incorrect information in their signature,they have more opportunities to correct such information and it can beconfirmed or refuted based on other signals or electronic activitiesprocessed by the node graph generation system 200. Furthermore, thecontribution score of a data point decreases as the data point getsolder or the date associated with the last update of the data pointsgets older as can be seen by the contribution scores of the data pointsshown in the table above.

The system 200 can be configured to compute confidence scores using thecontribution scores of individual data points identified by entries inthe value data structure of the value for which the confidence score isbeing generated. The system 200 can compute confidence scoresperiodically. In some embodiments, the system 200 can update theconfidence score of a value when additional data points are added. Insome embodiments, the system 200 can compute the confidence score of avalue based on a predetermined time schedule. The confidence score of avalue can be a function of the contribution scores of various datapoints supporting the value (i.e. included in the value data structureof the value). The confidence score of the value can also decrease overtime if no additional data points support the value. This is because thecontribution scores of the data points that support the value will getolder and since the contribution score of a data point is based onrecency, the contribution scores will decrease resulting in a decreasein the confidence score. As such, to maintain a high confidence score ofa value, newer entries need to be added to the value data structure. Viathis mechanism of maintaining a dynamically updated value data structurethat continually adds entries corresponding to data points that supportthe value, the node graph generation system 200 can continually computeand update a confidence score of a value based on the data pointsincluded in the corresponding value data structure.

By maintaining and periodically updating confidence scores of values,the system 200 can be configured to determine if electronic activitiesare correctly linked or matched to the right node profiles. Forinstance, if a given data point contributes to multiple field-valuepairs of the node profile and a predetermined number of values of thefield-value pairs have a confidence score below a threshold, the systemcan identify those data points that contribute to those values that havea confidence score below the threshold. The system can determine, foreach of those data points, how many values of fields of the node profiledoes that data point provide support. The system can then identify thosedata points as candidate data points that were correctly linked ormatched to the node profile. The system can then determine that the datapoint is improperly linked based on the number of values of the fieldsof the node profiles and the type of fields to which the data pointprovides support. The system can then unmatch or delink the data pointfrom the node profile by adjusting or recalculating the match score ofthe data point and the node profile. In this way, using the confidencescores of values, the system 200 can identify data points that wereincorrectly linked to the node profile thereby further improving theaccuracy of the node profile by removing data points that werepreviously incorrectly matched.

The systems described herein can also use confidence scores of values todetermine a status of a node profile at a given point in time, forinstance, 1:55 pm on Jun. 5, 2014. To do so, the node graph generationsystem 200 can discard all of the data points having a timestamp after1:55 pm on Jun. 5, 2014 and only use data points before 1:55 pm on Jun.5, 2014 that are included in the node profile. The node graph generationsystem 200 can then compute confidence scores of the field-value pairsof the node profile using the remaining or undiscarded data points todetermine a state of the node profile on 1:55 pm on Jun. 5, 2014. Thenode graph generation system 200 can use the confidence score of eachfield-value pair to determine the state of the node profile at the giventime. In this way, confidence state of a node profile can be determinedfor any point in time. As described herein, the node graph generationsystem 200 can make a request to determine a status of a node profile atany given point in time. For instance, the node graph generation system200 can determine the state of a node profile for John Smith on Dec. 20,2016. Similarly, the node graph generation system 200 can make a queryfor a particular value of a field of a node profile can be made for anypoint in time. Entries occurring after the particular time correspondingto the query can be filtered out so a value and its associatedconfidence score can be calculated using only those data points thathave a timestamp before the particular time.

By determining confidence scores of field-value pairs of node profiles,the node graph generation system 200 can be configured to executevarious types of requests based on the node profiles maintained by thenode graph generation system 200. For instance, the system can beconfigured to determine a list of node profiles that have a title ofDirector. The system can be configured to determine a list of nodeprofiles that have a title of Director on Jun. 1, 2015. The system canfurther be configured to determine a list of node profiles that work inSan Francisco and have a title of CEO. Moreover, the system can beconfigured to determine only those node profiles that have a Companyname of “ExampleCompany, Inc.” but with a confidence score for thatfield-value pair of above 90%. Using confidence scores as a thresholdfor selecting node profiles to be included in lists responsive toqueries and adjusting the confidence score threshold to see changes inthe lists can be a useful tool to identify node profiles with differentlevels of certainty.

In some embodiments, the present disclosure describes systems andmethods of updating confidence scores of values of fields based onelectronic activity includes associating the electronic activity to afirst value of a first field, assigning a first contribution score tothe electronic activity indicating a contribution level of theelectronic activity to a confidence score of the first value,associating the same electronic activity to a second value of a secondfield, assigning a second contribution score to the same electronicactivity indicating a contribution level of the electronic activity to aconfidence score of the second value, and updating the confidence scoresof the first value and the second value based on the first contributionscore of the electronic activity for the first value and the secondcontribution score of the electronic activity for the second value.

FIG. 27 illustrates a method 2700 to generate confidence scores ofvalues of fields based on data points. The method 2700 can includeaccessing a plurality of electronic activities or record objects (BLOCK2702). The method can identify, from a plurality of node profilesmaintained by the data processing system, a first node profile thatincludes a plurality of fields, each having corresponding one or morevalues (BLOCK 2704). The method can identify, for a given node profile,a field of the plurality of fields (BLOCK 2706). The method can thenidentify, for a given field of the plurality of fields of the nodeprofile, a given value of one or more values associated with the field(BLOCK 2708). The method can then identify, for the given value, a datapoint of one or more data points identified in the value data structureof the value (BLOCK 2710). The method can determine a contribution scoreof the data point (BLOCK 2712). The method can then determine, if thevalue data structure of the value includes any other data points thatsupport the value. If additional data points exist in the value datastructure, the method can determine the contribution score of theadditional data points until the contribution score of each of the datapoints of the value data structure are determined. If no other datapoints exist, the method can determine a confidence score of the valuebased on the contribution scores of one or more of the data pointssupporting the value (BLOCK 2714). The method can optionally determineif the field of the node profile includes additional values for which aconfidence score is to be determined. The method can then repeat thesteps from blocks 2708-2714. The method can similarly determine theconfidence scores of values of other fields by repeating Blocks2706-2014. The method can similarly determine the confidence scores ofvalues of other node profiles by repeating Blocks 2704-2714.

In further detail, the data processing system can access a plurality ofelectronic activities or record objects (BLOCK 2702). The dataprocessing system can access the electronic activities can access aplurality of electronic activities via one or more servers hosting orstoring the electronic activities. The servers can store electronicactivities transmitted from or received by accounts corresponding to anenterprise. For instance, the servers can be mail servers, phone logservers, calendar servers or any other entity that can store emails,calendar events, phone logs, or other electronic activities of accountsassociated with an enterprise, such as a company. The data processingsystem 9300 can be provided authorization to access the emails stored onone or more email servers through an API or an HTTP method (e.g., a GETmethod). Similarly, the data processing system can access record objectsof one or more systems of record. Each system of record can be managed,owned, maintained or otherwise accessed by an enterprise. The enterprisecan provide the data processing system access, permission or otherinformation that enables the data processing system to access dataincluded in the system of record. The data processing system can accessthe electronic activities and the record objects of one or more datasource providers.

The data processing system 9300 accessing the plurality of electronicactivities and/or the record objects of the system of record can furthermaintain a plurality of node profiles. The node profiles can berepresentations of nodes and includes fields that have values that aregenerated by data included in the plurality of electronic activitiesand/or record objects accessible by the system. The system can updatethe plurality of node profiles using at least one of the plurality ofelectronic activities or the plurality of record objects. Detailsregarding node profiles are described herein.

The method 2700 can include identifying a node profile from theplurality of node profiles maintained by the data processing system(BLOCK 2704). The data processing system can identify a node profile ofthe plurality of node profiles for which the system is to compute one ormore confidence scores for one or more values of the node profile. Thesystem can identify a particular node profile or can identify multiplenode profiles for which the confidence scores of values of the nodeprofile are to be determined. In some embodiments, the system can beconfigured to periodically compute confidence scores of node profiles.The system can identify a first node profile responsive to an update tothe first node profile or responsive to linking an electronic activityor record object to the first node profile. In some embodiments, thesystem can identify a first node profile responsive to an update to thefirst node profile or responsive to adding an entry identifying anelectronic activity or record object to a value data structure of avalue of a field of the first node profile.

The method 2700 can include identifying a field of the node profile(BLOCK 2706). The system can identify a field of the node profile forwhich to compute the one or more confidence scores for the one or morevalues of the field. In some embodiments, the system can identify afirst field of a plurality of fields of the identified node profileresponsive to an update to the field of the node profile or responsiveto adding an entry identifying an electronic activity or record objectto a value data structure of a value of the field.

The method 2700 can include identifying a value of the field for whichto determine a confidence score for which to compute the confidencescore (BLOCK 2708). In some embodiments, the system can identify a valueof one or more values associated with the field identified in BLOCK2706. The system can identify the value responsive to an update to thevalue of the field or responsive to adding an entry identifying anelectronic activity or record object to a value data structure of theidentified value.

In some embodiments, the value of the field of the node profile includesa first value of a first field of the node profile. The contributionscore of the data point can be a first contribution score of a firstdata point. The confidence score can be a first confidence score of afirst value. The data processing system 9300 can identify a second valuedata structure of a second field of the node profile. The second valuedata structure corresponds to a second value of the second field andfurther includes one or more second entries corresponding to respectiveone or more second data points that support the second value of thesecond value data structure. For example, and referring to Table 1, thevalue data structure corresponding to field-value pair First Name:Johnathan can include the value Johnathan and a first data point DPID101 and a second data point DP ID225. The data processing system 9300can determine, for at least one second data point of the one or moresecond data points of the second value of the second field of the nodeprofile, a second contribution score of the second data point based on atime corresponding to when the second data point was generated orupdated. For example, and referring to Table 1, a contribution score of0.4 can be determined for DP ID225 based on the time corresponding towhen the data was generated or updated (2/18/2017, 2 PM ET). The dataprocessing system 9300 can generate a second confidence score of thesecond value of the second field of the node profile based on the secondcontribution score of the at least one second data point. For example,and referring to Table 1, the confidence score of field-value pair FirstName: Johnathan can be updated based on the DP ID225.

To determine a confidence score of the value identified in BLOCK 2708,the method 2700 can include identifying a data point that supports theexistence of the value (BLOCK 2710). The data processing system canidentify a data point of the value by identifying an entry of a valuedata structure of the value. The entry can identify the data point, asource of the data point, a trust score of a source of the data pointand a timestamp associated with the data point. As described herein, forany given value of a field of a node profile, the value is associatedwith a value data structure that includes one or more entries. Eachentry of the one or more entries can correspond to one or more datapoints that include a string that matches the value of the value datastructure. For example, and referring to Table 1, for the field-valuepair First Name: John, the entry corresponding to Data Point 1 caninclude a string in the electronic activity EA-003 that matches thevalue John. Each data point of the one or more data points can identifya respective electronic activity of the plurality of electronicactivities or a respective record object of the plurality of recordobjects. For example, and referring to Table 1, for the field-value pairField: Director, Data Point 1 (DP ID101) can be associated withelectronic activity EA-003, Data Point 2 (DP ID2265) can be associatedwith system of record SOR-012, and Data Point 3 (DP ID243) can beassociated with electronic activity EA-117. The data point identifies anelectronic activity of the plurality of electronic activities or arecord object of a system of record previously linked by the dataprocessing system to the node profile associated with the value

The method 2700 can include determining a contribution score of the datapoint (BLOCK 2712). The data processing system can determine for atleast one data point of the one or more data points included in arespective value data structure of the value (determined in BLOCK 2708)of the field (determined in BLOCK 2706) of the node profile (determinedin BLOCK 2704), a contribution score of the data point based on a timecorresponding to when the data point was most recently generated orupdated. For example, and as illustrated in FIG. 26, the contributionscore CS_(a1) can be based on the time T_(a1) when the data pointDATA_PT_(a1) was generated or updated.

In some embodiments, the data processing system 9300 can determine forthe at least one data point of the one or more data points, thecontribution score of the data point includes determining, for the atleast one data point, a contribution score of the data point based on atrust score assigned to a source of the data point, the trust scoredetermined based on a type of source of the data point. For example, andwith reference to Table 1, the contribution score of DP ID101corresponding to field-value pair First Name: John is 0.6 and is basedon the trust score of 100. In some embodiments in which the data pointis a record object, the trust score assigned to the data point is basedon a health of the system of record from which the record object wasaccessed. In some embodiments, the health of the system of record fromwhich the record object was accessed is determined based on comparingfield values of object fields included in record objects of the systemof record to node profile field values of fields of one or more nodeprofiles having respective confidence scores above a predeterminedthreshold.

The method 2700 can include generating a confidence score based on thecontribution scores of the data points (BLOCK 2714). The data processingsystem 9300 can generate a confidence score of the value of the field ofthe node profile based on the contribution score of the at least onedata point. In some embodiments, the data processing system can generatethe confidence score of the value based on the contribution score ofeach of the data points identified in entries of the value datastructure of the value. For example, and as illustrated in FIG. 26, theconfidence score C₁ is a function of the contribution scores CS_(a1),CS_(a2), . . . , CS_(aN). The confidence score C₂ can be based on thecontribution scores CS_(b1), CS_(b2), . . . , CS_(bN). The confidencescore C₃ can be based on the contribution scores CS_(a1), CS_(a2), . . ., CS_(aN).

In some embodiments, a data point identifies an electronic activity isan automatically generated bounce back electronic activity. Examples ofbounce back electronic activity can include emails indicating that thedestination email address is invalid or incorrect, the person is nolonger with company, among others. In some embodiments, the node profileincludes a first field having a first value data structure identifying afirst value. The first value can be assigned to the first field bylinking a first electronic activity to the node profile

In some embodiments the data processing system can receive a secondelectronic activity. The data processing system can determine that thesecond electronic activity includes or supports the value of the fieldof the node profile. For example, and with reference to Table 1, anelectronic activity EA-098 can include a value John of the field FirstName. The data processing system can match the electronic activity tothe node profile and add an entry in one or more value data structurescorresponding to field-value pairs of the node profile that aresupported by the electronic activity. The system can determine whichfield-value pairs of the node profile are supported by the electronicactivity as this information is used for matching electronic activitiesto node profiles. the system can then determine a contribution score ofthe second electronic activity for each field-value pair of the nodeprofile that the second electronic activity supports. The dataprocessing system can generate a second contribution score of the secondelectronic activity for the value of the field of the node profile asdescribed herein. For example, and with reference to Table 1, acontribution score for DP ID458 associated with electronic activityEA-098 and field-value pair First Name: John can be generated. The dataprocessing system can update the confidence score of the value based onthe contribution score of the second electronic activity. The confidencescore for field-value pair First Name: John can be updated based on thecontribution score of DP ID458 that identifies the electronic activityEA-098.

In some embodiments, the data processing system can identify the firstelectronic activity. The first electronic activity can be linked to thefirst node profile by identifying from data included in the firstelectronic activity, a plurality of strings. For example, and asillustrated in FIG. 25, data included in electronic activity 652 a canbe identified. The strings that can be identified from the data caninclude “John Smith”, “Director”, “ACME”, “555-5439”, “617.555.2000”,“J@acme”, “a@acme”, and “Abigail.” In some embodiments, the electronicactivity includes a signature block in the electronic activity andlinking the electronic activity to the node profile includes the dataprocessing system 9300 extracting a plurality of strings from thesignature block of the electronic activity.

The data processing system can identify a plurality of candidate nodeprofiles to which to link the electronic activity by comparing one ormore strings of the plurality of strings to values of fields ofrespective candidate node profiles. For example, the data processingsystem can identify that node profile NPID-1 and node profile NPID-2 arecandidate node profiles because they contain field-value pairsassociated with the strings identified from the data included inelectronic activity 652 a. The data processing system can generate, foreach candidate node profile, a match score indicating a likelihood thatthe electronic activity is transmitted or received by an accountcorresponding to the candidate node profile based on comparing theplurality of strings included in the electronic activity to values offields included in the candidate node profile. The match score can bebased on a number of fields of the node profile including a value thatmatches a value or string in the electronic activity. The match scorecan also be based on different weights applied to different fields. Theweights may be based on the uniqueness of values of the field, asmentioned above. The data processing system can be configured to matchthe electronic activity to the node with the greatest match score. Insome embodiments, the data processing system can match the electronicactivity to each candidate node that has a match score that exceeds apredetermined threshold. Further, the data processing system canmaintain a match score for each electronic activity to that particularnode profile, or to each value of the node profile to which theelectronic activity matched. By doing so, the data processing system canuse the match score to determine how much weight to assign to thatparticular electronic activity. The data processing system 9300 can linkthe first electronic activity to the first node profile based on thematch score of the first node profile. For example, the strings “JohnSmith”, “Director”, “ACME”, “555-5439”, “617.555.2000”, and “j@acme” canhave a high match score to node profile NPID-1 and be linked to nodeprofile NPID-1. The strings “a@acme” and “Abigail” can have a high matchscore to node profile NPID-2 and can be linked to node profile NPID-2.

In some embodiments the data processing system 9300 can identify arecord object of a system of record previously not matched to the valueof the field of the node profile. The data processing system 9300 candetermine that the record object includes the value of the field of thenode profile. The data processing system can then add an entryidentifying the record object to a value data structure of the value.The data processing system 9300 can generate a contribution score of therecord object. The contribution score of the record object indicates alevel of contribution of the record object to the value of the field.For instance, if the value is the name “John” for the field “First Name”of a particular node profile, the record object can be identified in anentry of the value data structure of the field-value pair “First Name:John” for the particular node profile if the record object includes acorresponding name-value pair that supports the field-value pair “FirstName: John” for the particular node profile. To do so, the dataprocessing system will first have to match the record object to theparticular node profile and then add an entry to a value data structureof the field-value pair “First Name: John.” The data processing systemcan compute a new confidence score for the value based on thecontribution score of the record object.

In some embodiments the data processing system 9300 can receive asubsequent electronic activity. The data processing system 9300 can linkthe electronic activity to the node profile by including one or moreentries identifying the electronic activity to one or more value datastructures corresponding to one or more values of one or more fields.The data processing system can generate for each entry identifying theelectronic activity, a contribution score of the electronic activity,the entry corresponding to a respective value data structure of arespective value of a respective field. The data processing system cangenerate respective confidence scores for the values based on therespective contribution scores of the electronic activity.

It should be appreciated that as described herein, electronic activitiescan include electronic activities that are transmitted or received viaelectronic accounts, or data derived from such electronic activities.The data may be derived from the electronic activities by parsing theelectronic activities to extract information that can be used togenerate tags from the electronic activities, identify one or morefield-value pairs of node profiles, generate one or more activityfield-value pairs that can correspond to one or more participants of theelectronic activity, among others. The data can include tags, words,strings, field-value pairs, features or any other information that canbe extracted or otherwise derived from an electronic activity. Theelectronic activities can be parsed by entities other than the dataprocessing system 9300.

20. Inferring a Time Zone of a Node Profile Using Electronic Activities

The present disclosure relates to systems and methods for inferring atime zone of a node profile using electronic activities. An enterprise,company, or other entity may employ people in disparate geographicareas. For example, while an entity may have a headquarters located in aparticular city or region, there may be other offices for the entity indifferent areas of the country or international offices in other partsof the world. Some employees may also work remotely, either permanentlyor temporarily. In addition, employees may travel between offices or maytravel to meet clients or prospects in different locations or travel forpleasure. As a result, it can be challenging to determine a person'stime zone during a specified time period, even if other information isavailable for the person, such as a work address of the person, thelocation of a headquarters or primary office for the person's employer.

This disclosure describes techniques for inferring a person's time zonebased on the electronic activities the person sends during a specifiedtime period. For example, during a typical day, a person may send andreceive a number of emails, phone calls, or other electronic activitiesas a part of their employment. In some instances, a person may be morelikely to send and receive a larger volume of electronic activitiesduring traditional working hours, such as during the morning, afternoon,and early evening, and may be less likely to send and receive electroniccommunications outside of traditional working hours, such as overnight.The systems and methods described herein can access electronicactivities generated by a person within a specified time period, and canuse the electronic activities to determine the person's schedule andpredict the time zone in which the person was located during the timeperiod. In some implementations, the systems and methods describedherein can generate a temporal distribution of electronic activity fortime intervals within the time period. The systems and methods describedherein determine patterns based on the temporal distribution to inferthe person's time zone during the time period for which the electronicactivities were accessed.

As one example, a user, such as an employee, may initiate, transmit,receive, respond to, or otherwise participate in electronic activitiesover a specified period of time (e.g., from 9:00 A-5:00 P in the timezone in which the employer's headquarters is located). Another user whoworks at the same employee may participate in electronic activities overa second period of time (e.g., from 1:00 P-9:00 P in the time zone inwhich the employer's headquarters is located). A data processing systemmay identify electronic activities engaged in by the users. For eachuser, the system may generate a temporal distribution of electronicactivities for a time period (such as a day, a week, a month, or anyother time period). The system may determine an electronic activitypattern for each user based on the temporal distributions of electronicactivities for each user during the time period. The system maycorrelate the temporal distributions for each user with a respectivetime zone. For example, the system may determine that the first personis located in the time zone of the headquarters of the employer, whilethe second user is in a location that is time shifted from the time zoneof the headquarters of the employer, based on electronic activitypatterns for the users.

In some implementations, the system can use the inferred time zoneinformation to support or corroborate other types or forms ofinformation. For example, a system of record may store informationrelating to, relevant to, or correlated with time zone information. Insome implementations, the system of record can maintain field-valuepairs for record objects. A field-value pair may relate to a location ofa user associated with a record object. The system can use the time zoneinformation to support or corroborate such location information. Forexample, some systems of record may record a value of a location fieldusing a company headquarters location (e.g., city) as a default valuefor record objects associated with all employees of the company, eventhough some employees may work remotely or may work in offices that arenot at the headquarters location.

Determining or inferring a time zone for a person based on the person'selectronic activities can help to support such location information. Insome implementations, the systems and methods disclosed herein canincrease or decrease a confidence score associated with a field-valuepair of a record object or node profile, based on inferred time zoneinformation. For example, if a person's electronic activities indicatethat the person is in a time zone that does not correspond to a storedlocation value for the person, the systems and methods of thisdisclosure can reduce a confidence score associated with the storedlocation value. In some implementations, if a person's electronicactivities indicate that the person is in the same time zone as a storedlocation value for the person, the systems and methods of thisdisclosure can increase the confidence score associated with the storedlocation value. In some implementations, the systems and methods of thisdisclosure can update one or more values of field-value pairs, such as alocation field-value pair or a time zone field-value pair, based on theinferred time zone information.

In some implementations, the system can determine the electronicactivity pattern for the user based on electronic activities over alarge period of time. For instance, the system may rely on activitiesover a 90 day period of time. Doing so can help the system determinedays that the user may be on vacation, traveling, or otherwise outsideof the users usual time zone. In addition, the system can use theinferred time zone information to disambiguate between two or more userswho may share other characteristics (e.g., first name and last name) incommon with one another. For example, when the system accesses a newelectronic activity that could be attributed to any of several userseach having a respective candidate node maintained by the system, thesystem can compare a timestamp of the new electronic activity with atime zone of the candidate node profiles in order to select or determineone of the node profiles that is associated with the new electronicactivity.

In some implementations, the system can use an inferred time zone of anode profile in order to schedule delivery of other information to auser associated with the node profile. For example, notifications,messages, or any other information can be delivered to a user at a timeselected based in part on the inferred time zone. This can help toincrease a likelihood that the user will receive, view, or act on thedelivered information. For example, it may be desirable to provideinformation such as a notification to the user at a time (or during awindow of time) when the user is likely to be able to receive, view, oract on the notification, such as during daytime hours or during typicalwork hours in the user's time zone. If the notification is instead sentduring different hours (e.g., during the night), the user may be lesslikely to receive, view, or act on the notification. Thus, in someimplementations, the system can identify one or more restrictednotification periods based on the determined time zone. The restrictednotification periods may be any periods that are determined to beperiods during which the user is unlikely to receive, view, or act on anotification, such as during the early morning or night time hours inthe user's time zone. In some the system can restrict transmission ofthe notification to the computing device of the user associated with thenode profile during the restricted notification period. The system canschedule delivery of the notification to the computing device of theuser during a time period that is not restricted, based on the inferredtime zone.

According to the embodiments of the systems and methods describedherein, the system can parse and process electronic activities to inferor otherwise determine a time zone for a person. The system can assign,correlate, or otherwise associate the time zone with a node profile forthe user. The system can use information from various sources ofelectronic activity for generating temporal distributions of electronicactivity for each user. The temporal distributions for each user mayeach be associated with a respective time interval. The system canidentify the time zone for a user based on the temporal distributions.Various other benefits and advantages of the present technical solutiondescribed further below.

Referring now to FIG. 28, illustrated is a use case diagram 2800 forinferring a time zone of a node profile based on electronic activities.The diagram 2800 depicts a node profile 2804 associated with a user whoparticipates in a plurality of electronic activities 2806 during a timeperiod (e.g., a day, a week, a month, etc.). The user corresponding tonode profile 2804 may participate in electronic activities 2806 withvarious other users corresponding to other nodes 2808 a-2808 n withinthe time interval. Each user may include a corresponding node profile2804 (e.g., a node profile 2804 corresponding to “John Smith”, and nodeprofiles for each of the users who are participants with “John Smith” inelectronic activities 2806(1) -2806(n)). In some implementations, atleast some of the operations described in connection with the diagram2800 may be performed by a system such as the node graph generationsystem 200 of FIG. 4. For example, as described in greater detail above,the system 200 may be configured to generate the node profiles 2804based on electronic activity. In the use case diagram depicted in FIG.28, a first user (e.g., “John Smith”) may generate and send a number ofelectronic activities at various times to various other users. The usecase diagram shows a plurality of different types of electronicactivities (e.g., phone calls, emails, electronic calendar events, etc.)conducted between John Smith and various other users. As described ingreater detail below, the system 200 may be configured to determine,identify, or otherwise infer a time zone for the node profile 2804 basedon the electronic activities engaged in by a corresponding user (e.g.,John Smith) over the time interval.

The system 200 may be configured to receive, detect, identify, register,collect, or otherwise access electronic activities 2806. Each electronicactivity 2806 may be generated, transmitted, or received by at least oneparticipant. In some instances, an electronic activity 2806 may includea plurality of participants. For instance, a user initiating an email toone recipient may be the sole participant of the electronic activity2806 (e.g., the one participant being the sender of the email). Asanother example, a conference call, meeting, phone call, etc. mayinclude two or more participants (e.g., attendees or participants of theconference call, meeting, phone call, etc.). In instances in which aphone call is initiated and not answered, the corresponding electronicactivity 2806 may have one participant (e.g., the person initiating thephone call). In instances in which a phone call is answered, thecorresponding electronic activity 2806 may have two or more participants(e.g., the person initiating the phone call and the person(s) answeringthe phone call).

The system 200 may be configured to detect, receive, or otherwiseidentify electronic activities (such as electronic activity 2806)exchanged, transmitted, received, or otherwise engaged in by a usercorresponding to a node profile. As described in greater detail above,the system 200 may include an electronic activity ingestor 205configured to ingest electronic activities 2806 from a plurality of datasource providers. The electronic activities 2806 may be received oringested in real-time or asynchronously as electronic activities aregenerated, transmitted, or stored by the one or more data sourceproviders. The data source providers each may be or may include aserver, which hosts a domain corresponding to one or more of theparticipants of an electronic activity 2806 (e.g., the sender or therecipient). The system 200 may identify electronic activities 2806 thatidentify a sender, one or more recipient(s), and include a body orcontent. As described in greater detail below, the electronic activityparser 210 of the system 200 may be configured to parse the identifiedelectronic activities. The electronic activity parser 210 may parse theelectronic activity to identify a timestamp for each electronic activity2806 sent, generated, or otherwise initiated by a user associated with anode profile 2804. The system 200 may be configured to generate atemporal distribution for each time interval in a time period based onthe timestamps. The system 200 may be configured to determine anelectronic activity pattern based on the temporal distributions over thetime period. The system 200 may be configured to identify a region ofthe temporal distribution corresponding to a schedule of the user byapplying a region identification policy to the electronic activitypattern.

The system 200 may be configured to select, determine, locate, orotherwise identify each electronic activity 2806 sent, received, orotherwise engaged in by the user corresponding to the node 2802. Thesystem 200 may be configured to identify each electronic activity 2806associated with the node profile 2804 for the user. The node profile2804 may include a plurality of fields having values corresponding toaccounts of the user. For instance, in the example depicted in FIG. 28,the node profile 2804 includes a value “J@ACME.COM” for a fieldassociated with an email address, a value “555.555.5439” for a fieldassociated with a work phone number, and a value “617.555.2000” for afield associated with a cell phone number. The electronic activityparser 210 may parse each electronic activity 2806 to identify a set ofelectronic activities in which John Smith was a participant based onwhich electronic activities indicate a sender or recipient having avalue that matches at least one of the values of a corresponding fieldassociated with the node profile 2804 (e.g., a phone call dialed by oranswered via a phone number having a value that matches the valuecorresponding to the field associated with work phone number or cellphone number, an email sent via an email address having a value thatmatches the value corresponding to the field associated with the emailaddress, and so forth).

The system 200 may parse each of the electronic activities 2806 sent,received, or otherwise engaged in by the user corresponding to the node2802. In some embodiments, the electronic activity parser 210 may parsea header or metadata for an electronic activity 2806 to determineinformation relating to the electronic activity 2806. For example, theelectronic activity parser 210 may parse the header or metadata for eachelectronic activity 2806 to identify a timestamp associated with eachelectronic activity 2806. As stated above, each electronic activity 2806may include a corresponding timestamp. The timestamp may be or mayindicate the time at which the electronic activity 2806 was generated,sent, received, stored, etc. The electronic activity parser 210 may beconfigured to parse each electronic activity 2806 to identify thetimestamp associated with each respective electronic activity.

In some embodiments, the system 200 may be configured to determine atype of electronic activity for each electronic activity 2806 sent,received, or otherwise engaged in by the user corresponding to the node2802. In some embodiments, the tagging engine 265 may be configured totag each electronic activity 2806 with a tag corresponding to the typeof electronic activity. For instance, the tagging engine 265 may tag anelectronic activity 2806 as an email if the electronic activity 2806 wastransmitted via an electronic account included as a value of an emailaddress field of the node profile 2804. The system 200 may determine thetype of electronic activity for each electronic activity 2806 sent,received, or otherwise engaged in by the user corresponding to the node2802 based on the tag for each electronic activity. As another example,the electronic activity parser 210 may be configured to determine thetype of electronic activity for each electronic activity 2806. Asdescribed above, the electronic activity parser 210 may parse electronicactivity to identify a set of electronic activities in which a user wasengaged based on which electronic activities indicate a sender orrecipient having a value that matches at least one of the values of acorresponding field associated with the node profile 2804. In someembodiments, the electronic activity parser 210 may store an associationbetween each field of the node profile 2804 and a corresponding type ofelectronic activity. The electronic activity parser 210 may identify thetype of electronic activity for each electronic activity in the setbased on the association between the field of the node profile 2804 andwhich value was used for including the electronic activity 2806 in theset. For example, the electronic activity parser 210 may store anassociation between the field “EMAIL” and the type of electronicactivity “EMAIL.” The electronic activity parser 210 may identifyelectronic activities for including in the set of electronic activitieslinked to the node profile 2804 based on which electronic activitieswere sent or received by “J@ACME.COM.” The electronic activity parser210 may similarly identify the type of each electronic activity in theset as an email based on which electronic activities which were sent orreceived by “J@ACME.COM” and the association with the correspondingfield “EMAIL” and type of electronic activity “EMAIL.” In a similarmanner, the electronic activity parser 210 may also determine whethereach electronic activity 2806 is a phone call, a text message, anelectronic calendar event, etc. In some implementations, the electronicactivity parser 210 can determine a type for an electronic activity 2806based on a data format or file extension of a computer file associatedwith the electronic activity 2806.

In some embodiments, the electronic activity parser 210 may beconfigured to parse the electronic activity 2806 to determine a locationcorresponding to the user when the user engaged in the electronicactivity. The electronic activity 2806 may include metadatacorresponding to an IP address of the client device which was used forgenerating, transmitting, sending, or otherwise engaging in theelectronic activity 2806. As another example, the electronic activity2806 may include metadata corresponding to a network address, networkname, etc. to which the client device was connected when the user wasengaging in the electronic activity. The electronic activity parser 2806may parse the metadata to extract such information for identifying,determining, or otherwise inferring a location of the user when the userengaged in the electronic activity. The electronic activity parser 210may maintain, include, or otherwise access a list, ledger, database,etc. of IP addresses of enterprise client devices, enterprise networkaddresses, enterprise network names, etc. associated with the nodeprofile 2804 for the user. The electronic activity parser 210 maycompare the data extracted from an electronic activity 2806 to the dataincluded in such a database to determine whether the electronic activitywas engaged in on the enterprise client device of the user, theenterprise network, etc. If the electronic activity parser determinesthat the IP address, network address, network name, etc. matches thedata in the database, the electronic activity parser 210 may determinethe electronic activity was engaged in (e.g., generated, sent, received,etc.) at a location associated with the enterprise. Where the electronicactivity parser 210 does not identify a match, the electronic activityparser 210 may determine the electronic activity was engaged in at alocation other than the location of the enterprise. The electronicactivity parser 210 may determine, identify, or infer the location inwhich the user engaged in the electronic activity for applying weightsto the electronic activity in generation of an electronic activitypattern for the user.

The system 200 may be configured to arrange, sort, filter, compile, orotherwise identify the electronic activities 2806 generated by the userwithin a time period. In some embodiments, the system 200 may include atemporal distribution generation engine. The temporal distributiongeneration engine may be or include any script, file, program,application, set of instructions, or computer-executable code that isconfigured to determine, identify, compile, produce, or otherwisegenerate a temporal distribution of electronic activity for a userwithin the time period. The time period may be, for instance, a day(e.g., a 24 hour period), a week (e.g., a working week from Mondaythrough Friday), a month (e.g., a 30-day period), and so forth. In someimplementations, the temporal distribution generation engine may beembodied on or otherwise be a component or element of the electronicactivity parser 210. The temporal distribution generation engine may beconfigured to generate the temporal distribution of electronic activityfor a series of time intervals within a time period (e.g., within amonth window, a 90 day window, a 180 day window, and so forth). Hence, atime period may be made up of a group of time intervals.

The temporal distribution generation engine may be configured togenerate the temporal distribution of electronic activity based on theidentified timestamps for each of the electronic activity in the set ofelectronic activity for a user. The electronic activity parser 210 maybe configured to identify the timestamp for each electronic activitygenerated, sent, received, or otherwise engaged in by the user. Thetemporal distribution generation engine may be configured to generatethe temporal distribution by compiling each electronic activity engagedin by the user having a timestamp which falls within a time interval.The temporal distribution generation engine may sort, compile, orotherwise filter each of the electronic activities within the set ofelectronic activities by timestamp. The temporal distribution generationengine may maintain, for each time interval within the time period, asubset of electronic activities having timestamps which fall within therespective time intervals. Hence, a given time interval may have anynumber of electronic activities based on which electronic activitieswere engaged in by the user within that time interval.

Continuing the example shown in FIG. 28, John Smith is shown to haveparticipated in two electronic activities (a phone call EA1 and an emailEA2) with a second node (e.g., N2 at T(1) and T(2), respectively) andtwo electronic activities (a phone call EA3 and a meeting EA4) with athird node (e.g., N3 at T(3) and T(4), respectively). John Smith mayengage in any number of electronic activities at various times within afirst time interval (e.g., Time Interval (1)). The temporal distributiongeneration engine may compile each of the electronic activities engagedin by John Smith for the first time interval based on the timestamps(e.g., T(1)-T(N)) which fall within the time interval (e.g., TimeInterval (1)). The temporal distribution generation engine may sort,arrange, or organize each of the electronic activities compiled for thefirst time interval by their respective timestamps.

Referring now to FIGS. 28 and 29, the temporal distribution generationengine may compile, produce, or otherwise generate data corresponding toa graphical representation 2902 of the temporal distributions.Specifically, FIG. 29 depicts a graph 2900 showing a plurality ofgraphical representations 2902 of temporal distributions 2810 ofelectronic activity for various time periods. The temporal distributiongeneration engine may generate a temporal distribution 2810(1) ofelectronic activities for the first time period (Time Period (1)). Insome implementations, each electronic activity within the subset ofelectronic activities falling within the first time period may bearranged within a time interval (e.g., an hour) of the first time period(e.g., a 24 hour period). The temporal distribution generation enginemay generate the temporal distribution 2810(1) by grouping eachelectronic activity in the subset by which time interval of the firsttime period the respective timestamps fall within. As shown in FIGS. 28and 29, for the first time period (Time Period (1)), John Smith may nothave engaged in any electronic activity (e.g., did not send, initiate,or otherwise actively participate in any electronic activity) betweentime intervals beginning at 00:00-07:00. However, in the example shownin FIGS. 28 and 29, John Smith engaged in two electronic activities (forinstance, participated in a call, responded to an email, etc.) withinthe time interval between 8:00-8:59, seven electronic activities withinthe time interval between 9:00-9:59, 13 electronic activities within thetime interval between 10:00-10:59, and so forth through 18:00-18:59(e.g., 6:00 P-6:59 PM). Following 18:59, John Smith may not have engagedin any electronic activity for the rest of the time period (e.g., from19:00-23:59). The graphical representation 2902(1) corresponding to thetemporal distribution 2810(1) shows the electronic activities generated,initiated, participated in, or otherwise engaged in by the user (e.g.,John Smith) over the first time period. As shown in FIG. 29, thetemporal distribution generation engine may generate graphicalrepresentations 2902(1)-2902(3) of the temporal distributions2810(1)-2810(3) of electronic activity at various time periods (TimePeriod (1)-Time Period (3)).

The temporal distribution generation engine may be configured togenerate, for each time period, a respective temporal distribution 2810.While charts representing three temporal distributions are depicted inFIG. 28 and FIG. 29, it is noted that the temporal distributiongeneration engine may generate any number of temporal distributions 2810depending on the number of time intervals within the time period. Insome implementations, the temporal distribution generation engine maygenerate temporal distributions 2810 for time periods corresponding tobusiness days. The temporal distribution generation engine may maintain,include, or otherwise access a list of business days corresponding to anenterprise associated with the user. The list of business days may be,for instance, Monday through Friday. In some implementations, the listof business days may be a preset list of business days. In someembodiments, a person associated with the enterprise, such as anadministrator, the user, etc., may update, modify, or otherwise changethe preset list of business days based on scheduled non-business days(e.g., holidays), days off, and so forth. In some embodiments, thetemporal distribution generation engine may be configured toautomatically detect (e.g., based on deviations from an electronicactivity pattern for the user) non-business days. The temporaldistribution generation engine may generate temporal distributions foreach time period which corresponds to a business day. The temporaldistribution generation engine may forego generation of (or disregard,delete, or otherwise remove already generated) temporal distributions2810 of electronic activity corresponding to non-business days.

In some embodiments, the system 200 may compare at least one temporaldistribution for a node profile to one or more other temporaldistributions stored in a database or data structure and associated witha respective time zone. The database may include electronic activitytemporal distributions corresponding to each of a plurality of timezones (e.g., 24 temporal distributions each corresponding to arespective time zone). In some implementations, the stored temporaldistributions may be referred to as temporal distribution templates. Thesystem 200 may identify the temporal distribution template included inthe database that most closely matches at least one temporaldistribution of the user. For example, the system 200 may identify whichtemporal distribution template of the database most closely matches thetemporal distribution of the user based on which temporal distributiontemplate tracks the temporal distribution of the user, which temporaldistribution template has or includes upticks and downticks inelectronic activity within the same time frame as the temporaldistribution of the user, and so forth. When a match is determined, thesystem 200 may infer that the time zone for the user is the same as thetime zone of the matching temporal distribution template, based on thematch.

In some implementations, the temporal distribution templates can begenerated or selected based on electronic activities associated withnode profiles having known time zone information. For example, togenerate a temporal distribution template for a given time zone, thesystem 200 may identify at least one node profile that is known to beassociated with a user in that time zone. The system 200 may then accessa set of electronic activities for the identified node profile todetermine at least one temporal distribution of electronic activitiesfor the node profile, in a manner similar to that described above, andthe at least one temporal distribution may be stored as a temporaldistribution template for that time zone. In some implementations, thesystem 200 may store more than one temporal distribution template for agiven time zone. For example, the system 200 may store a respectivetemporal distribution template for each of a plurality of differentindustries, job titles, or any other characteristics of node profiles.In some implementations, typical electronic activity patterns may varyacross industries or job titles, even within the same time zone. Thus,in order to infer a time zone of a user based on the users electronicactivities, it may be useful to compare the users temporal distributionof electronic activities to a corresponding temporal distributiontemplate representing one or more node profiles of users in the sameindustry or with the same job title. To achieve this, the system 200 maygenerate and store temporal distribution templates for different jobtitles, industries, or any other criteria within a given time zone. Toinfer a time zone for a particular node profile, the system 200 mayselect one or more temporal distribution templates to compare with thetemporal distribution of the node profile based on match between anindustry or job title of the node profile and a corresponding industryor job title of the one or more temporal distribution templates.

In some implementations, instead of comparing a single temporaldistribution of electronic activities of a node profile with anothertemporal distribution (e.g., a temporal distribution template), thesystem 200 may determine a temporal distribution pattern for the nodeprofile based on a plurality of temporal distributions, and the patterncan be compared to a corresponding pattern known to be associated withparticular time zones in order to determine a match. For example, thesystem 200 may be configured to generate, identify, or otherwisedetermine an electronic activity pattern for the user based on more thantemporal distribution of electronic activities for the user. In someembodiments, the system 200 may include a pattern determination engine.The pattern determination engine may be or include any script, file,program, application, set of instructions, or computer-executable codethat is configured to generate, identify, compile, produce, or otherwisedetermine an electronic activity pattern for the user within the timeperiod. The pattern determination engine may determine the electronicactivity pattern based on each of the temporal distributions 2810generated by the temporal distribution generation engine that fallwithin the time period. The pattern determination engine may determinethe electronic activity pattern by applying a pattern determinationpolicy to each of the temporal distributions within the time period. Thepattern determination policy may be a rule-based policy, a probabilisticmodel, etc. The pattern determination policy may be generated by anadministrator defining the rules, generated via a machine learningalgorithm using a training set of temporal distributions andcorresponding electronic activity patterns, and so forth.

The pattern determination engine may be configured to apply the patterndetermination policy to each of the temporal distributions within thetime period for generating the electronic activity pattern for the user.As described in greater detail below, the system 200 may be configuredto determine a region of the time interval corresponding to a scheduleof the user based on the electronic activity pattern for the user.

The pattern determination engine may use the pattern determinationpolicy to compute an average, mean, or other statistic of electronicactivity for each time frame within a respective time interval in thetime period. In some embodiments, the pattern determination engine mayuse the pattern determination policy to filter one or more data outliersin the temporal distribution (e.g., a number of electronic activitiesexceeding a standard deviation from the average or mean of electronicactivity within a respective time frame). In some embodiments, thepattern determination engine may use the pattern determination policy tofilter electronic activities within a respective time frame having amean or average less than one electronic activity. Continuing theexample depicted in FIG. 28, the pattern determination engine may applythe pattern determination policy to each of the temporal distributions2810(1)-2810(3) to identify an electronic activity pattern 2812. Theelectronic activity pattern 2812 may be an average of electronicactivity within each time frame (e.g., 8:00-19:59) for each timeinterval (Time Interval (1)-Time Interval (3)). The patterndetermination engine may filter, from the electronic activity pattern2812, data points for time frames with average electronic activity lessthan one (e.g., time frame 19:00-19:59 has an average electronicactivity of 0.66, which is less than one). The pattern determinationengine may be configured to generate a graphical representation 2904 ofthe electronic activity pattern 2812 on the graph 2900. As can be seenin FIG. 29, the electronic activity pattern 2812 may track, trend, orotherwise follow the temporal distributions of electronic activity foreach time interval within the time period.

In some implementations, the pattern determination engine may use thepattern determination policy to leverage data from the electronicactivity for applying weights to different electronic activities withinthe respective temporal distributions. In some embodiments, the patterndetermination engine may use the pattern determination policy to applyweights to electronic activity based on the determined type ofelectronic activity, the complexity of electronic activity, and/or thelocation in which the electronic activity was engaged.

The pattern determination engine may use the pattern determinationpolicy to apply weights to electronic activity within the temporaldistributions based on the determined type of electronic activity. Thepattern determination engine may use the pattern determination policy touse the identified type of electronic activity within the time intervalfor assigning weights to each electronic activity. In such embodiments,the weights may be indicative of, correspond to, or otherwise associatedwith a level of engagement of the user in engaging in the electronicactivity. For instance, the pattern determination engine may use thepattern determination policy to apply weights that favor electronicactivity corresponding to participating in meetings greater thanelectronic activity corresponding to emails. As another example, thepattern determination engine may use the pattern determination policy toapply weights that favor electronic activity corresponding to phonecalls or conference calls greater than electronic activity correspondingto emails. The pattern determination engine may use the patterndetermination policy to generate the electronic activity pattern asdescribed above following applying weights to the respective electronicactivities in each temporal distribution based on the type of electronicactivity.

The pattern determination engine may use the pattern determinationpolicy to apply weights to the electronic activity based on thecomplexity of each electronic activity. The pattern determination enginemay use the pattern determination policy to use the identifiedcomplexity of electronic activity within the time interval for assigningweights to each electronic activity. In such embodiments, the weightsmay be indicative of, correspond to, or otherwise be associated with alevel of engagement of the user in engaging in the electronic activity.The pattern determination engine may use the pattern determinationpolicy to leverage a language complexity score and/or word/charactercount generated, determined, or otherwise identified by the electronicactivity parser 210 for applying weights to the electronic activity. Thepattern determination engine may use the pattern determination policy toinclude, maintain, or otherwise access a database or set of rulescorresponding to language complexity scores, word or character counts,etc. and associated weights. For instance, the pattern determinationengine may use the pattern determination policy to apply weights whichfavor electronic activity having a higher complexity score, higher wordor character count, etc. Hence, the weights may increase with thelanguage complexity score, word count, character count, etc. As thelanguage complexity score, word count, character count, etc. increases,the level of engagement of the user in engaging in the electronicactivity may be presumed, assumed, or inferred to correspondinglyincrease. The pattern determination engine may use the patterndetermination policy to generate the electronic activity pattern asdescribed above following applying weights to the respective electronicactivities in each temporal distribution based on the complexity ofelectronic activity.

The pattern determination engine may use the pattern determinationpolicy to apply weights to the electronic activity based on the locationin which the electronic activity was generated. The patterndetermination engine may use the pattern determination policy to use thelocation in which the electronic activity was generated, sent, orotherwise engaged in by the user for assigning weights to eachelectronic activity. For instance, the pattern determination engine mayuse the pattern determination policy to apply weights which favorelectronic activity engaged in at a location associated with anenterprise corresponding to the user. As another example, the patterndetermination engine may use the pattern determination policy to applyweights which disregard, discard, filter, or otherwise ignore electronicactivity generated at a location other than a location associated withthe enterprise. The pattern determination engine may use the patterndetermination policy to generate the electronic activity pattern asdescribed above following applying weights to the respective electronicactivities in each temporal distribution based on the location in whichthe user was engaged in the electronic activity.

The system 200 may be configured to determine, select, or otherwiseidentify a region of interest of the electronic activity pattern. Insome embodiments, the system 200 may include a region identificationengine to perform this functionality. The region identification enginemay be or include any script, file, program, application, set ofinstructions, or computer-executable code that is configured togenerate, determine, compile, select, or otherwise identify a particularregion of the electronic activity pattern for the user. The region maybe associated with or otherwise correspond to an inferred schedule ofthe user. The region identification engine may apply a regionidentification policy to the electronic activity pattern for identifyingthe region. The region identification engine may apply the regionidentification policy to the electronic activity pattern for determiningwhich portions of the time interval the user is usually, normally,typically, etc. engaged in electronic activity. The system 200 mayidentify the region for inferring a work schedule of the user with thework schedule being correlated with the time interval in which the useris typically engaged in electronic activity.

In some embodiments, the region identification policy may be or includea probabilistic model. The probabilistic model may be generated via amachine learning algorithm trained based on a plurality of electronicactivity patterns and corresponding work schedules. The machine learningalgorithm may receive, as an input, a plurality of electronic activitypatterns for different users and their respective work schedules (whichmay be manually entered, for instance.). The machine learning algorithmmay output a probabilistic model which is configured to receive a givenelectronic activity pattern for a user and output a corresponding regionof a time interval which is likely to correspond to a work schedule forthe user. The region identification policy may refine the probabilisticmodel over time as subsequent work schedules are verified. The regionidentification policy may update the probabilistic model by retrainingthe probabilistic model via the machine learning algorithm.

In some embodiments, the region identification policy may leveragethresholds of electronic activity within a time frame of the timeinterval for identifying the region. The region identification policymay include, maintain, or otherwise access a first thresholdcorresponding to a first time of the time interval and a secondthreshold corresponding to a second time of the time interval. Theregion identification policy may be used by the system to apply thefirst and second thresholds to the electronic activity pattern toidentify the first and second time of the time interval. In someembodiments, the threshold may be or correspond to a change inelectronic activity over a time frame. In some embodiments, thethresholds may correspond to an average amount of electronic activityover two adjacent time frames. In some embodiments, the thresholds maycorrespond to a slope of the graphical representation 2904 of theelectronic activity pattern. The region identification policy may beused by the system to apply the first and second thresholds to theelectronic activity pattern to identify a first time corresponding to anuptick in electronic activity and a second time corresponding to adowntick in electronic activity engaged in by the user. The regionidentification policy may be used by the system to select the region ofthe time interval as spanning the first and second time.

In some embodiments, the region identification policy may be used by thesystem to compare the electronic activity pattern for a node profile toone or more other electronic activity patterns stored in a database ordata structure and associated with respective regions of the timeinterval. The database may include electronic activity patterns andrespective regions of the time interval similar to the datasets used totrain the machine learning algorithm for producing the probabilisticmodel as described above. The region identification policy may be usedby the system to compare the electronic activity pattern of the userwith the other electronic activity pattern(s) included in the database.The region identification policy may identify the region of the timeinterval based on which electronic activity pattern included in thedatabase most closely matches the electronic activity pattern of theuser. The region identification policy may be used by the system toidentify which electronic activity pattern of the database most closelymatches the electronic activity pattern of the user based on whichelectronic activity pattern tracks the electronic activity pattern ofthe user, which electronic activity pattern has or includes upticks anddownticks in electronic activity within the same time frame as theelectronic activity pattern of the user, and so forth. The regionidentification policy may be used by the system to identify the regionof the time interval corresponding to the electronic activity patternwhich most closely matches the electronic activity pattern of the user.

In some embodiments, the system 200 may compare at least one electronicactivity pattern for a node profile to one or more other electronicactivity pattern stored in a database or data structure and associatedwith a respective time zone. The database may include electronicactivity patterns corresponding to each of a plurality of time zones(e.g., 24 electronic activity patterns each corresponding to arespective time zone). In some implementations, the stored electronicactivity patterns may be referred to as electronic activity patterntemplates. The system 200 may identify the electronic activity patterntemplate included in the database that most closely matches at least oneelectronic activity pattern of the user. For example, the system 200 mayidentify which electronic activity pattern template of the database mostclosely matches the electronic activity pattern of the user based onwhich electronic activity pattern template tracks the electronicactivity pattern of the user, which electronic activity pattern templatehas or includes upticks and downticks in electronic activity within thesame time frame as the electronic activity pattern of the user, and soforth. When a match is determined, the system 200 may infer that thetime zone for the user is the same as the time zone of the matchingelectronic activity pattern template, based on the match.

In some implementations, the electronic activity pattern templates canbe generated or selected based on electronic activities associated withnode profiles having known time zone information. For example, togenerate an electronic activity pattern template for a given time zone,the system 200 may identify at least one node profile that is known tobe associated with a user in that time zone. The system 200 may thenaccess a set of electronic activities for the identified node profile todetermine a plurality of temporal distributions of electronic activitiesfor the node profile, and may further determine an electronic activitypattern for the node profile. The system 200 may store the determinedelectronic activity pattern as an electronic activity pattern templatefor that time zone. In some implementations, the system 200 may storemore than one electronic activity pattern template for a given timezone. For example, the system 200 may store a respective electronicactivity pattern template for each of a plurality of differentindustries, job titles, or any other characteristics of node profiles.In some implementations, typical electronic activity patterns may varyacross industries or job titles, even within the same time zone. Thus,in order to infer a time zone of a user based on the users electronicactivities, it may be useful to compare the user's electronic activitypattern to a corresponding electronic activity pattern templaterepresenting one or more node profiles of users in the same industry orwith the same job title. To achieve this, the system 200 may generateand store electronic activity pattern templates for different jobtitles, industries, or any other criteria within a given time zone. Toinfer a time zone for a particular node profile, the system 200 mayselect one or more electronic activity pattern templates to compare withthe electronic activity pattern of the node profile based on matchbetween an industry or job title of the node profile and a correspondingindustry or job title of the one or more electronic activity patterntemplates.

In some embodiments, the system 200 may be configured to identify valuesfrom field-value pairs of the node profile 2804 associated with theuser. The system 200 may be configured to identify a value correspondingto a field-value pair associated with a job title for the user, afield-value pair associated with an industry of the enterprise orcompany in which the user is employed, a location of the enterprise orcompany, etc. The region identification engine may identify the regionassociated with the schedule of the user based on the values from thenode profile 2804 for the user. In some implementations, the regionidentification engine may identify the region based on the job title ofthe user as reflected in the node profile 2804 of the user. Forinstance, where the job title indicates a higher level position, theuser may typically work longer or more consistent hours. On the otherhand, where the job title indicates a lower level position, the user maywork fewer hours, may be part-time, may work less consistent hours, andso forth. As another example, the industry in which the user works mayreflect more work from home options, more flexible working hours,time-shifted hours (e.g., relative to other industries in the same timezone), and so forth. The region identification engine may leverage thedata from the node profile 2804 of the user for modifying, adapting, orotherwise changing the region of the time interval. The regionidentification engine may expand the region when the user is a higherlevel position, adjust one or more weights of the temporal distribution(e.g., the weights based on the location of the user) based on theindustry, and so forth. Each of these implementations may use data fromthe node profile 2804 of the user to more accurately select or modifythe region of the time interval corresponding to the schedule of theuser.

The region identification engine may be configured to store anassociation between the node profile 2804 of the user and the identifiedregion of the time interval corresponding to the schedule of the user.The region identification engine may store the association in adatabase, memory, server, or other data structure. In some embodiments,the identified region may be static (e.g., once the region isidentified, the region remains the same). In some embodiments, theidentified region may be dynamic (e.g., the region may be changed overtime). In some embodiments, the region identification engine may updatethe region at various points in time. The region identification enginemay update the region on a schedule (e.g., once every six months, onceevery year, etc.). The region identification engine may update theregion upon occurrence of a condition which triggers the update. Thecondition may be a job change, a promotion, and so forth. The system 200may identify such conditions based on changes in confidence score ofvalues corresponding to fields of the node profile 2804, as describedabove in Section 1(D) above.

In some embodiments, the system 200 may leverage the region associatedwith the node profile 2804 of the user for identifying an eventcondition. In some embodiments, the system 200 may include an eventcondition detection engine. The event condition detection engine may beor include any script, file, program, application, set of instructions,or computer-executable code that is configured to determine, identify,or otherwise detect an event condition based on a comparison of temporaldistributions to an electronic activity pattern of a user. The eventcondition may be or correspond to vacation, a sick day, leave (e.g.,maternity, paternity, adoption, etc.), and so forth. Followinggeneration of the electronic activity pattern by the patterndetermination engine, the event condition detection engine may leveragethe electronic activity pattern to detect event conditions. In someembodiments, the event condition detection engine may include, maintain,or otherwise access an event condition detection policy. The eventcondition detection policy may be or include a rule-based policy, aprobabilistic model, etc. The event condition detection engine may beconfigured to apply the event condition detection policy to subsequenttemporal distributions to determine whether the temporal distributionsatisfies the policy for detecting a corresponding event condition.

In some embodiments, the event condition detection policy may include amachine learning model. The machine learning model may be a model whichis generated, produced, or otherwise trained to classify temporaldistributions of electronic activity. The machine learning model may betrained based on data sets, samples, or other training examples ofelectronic activity patterns or temporal distributions (or series oftemporal distributions) and corresponding labels associated withrespective event conditions. For instance, the machine learning modelmay be trained with a set of training examples corresponding to a firstevent condition (e.g., a sick day) using a plurality sample temporaldistributions corresponding to the first event condition (e.g., the sickday). Each sample temporal distribution may be tagged or labeled withthe respective event condition (e.g., a sick day label). Continuing thisexample, where a person is taking a sick day, the overall number ofelectronic activity engaged in by the person may decrease for the sickday. The machine learning model may be trained with temporaldistributions corresponding to the sick day such that, where a temporaldistribution corresponding to a sick day is provided as an input to themachine learning model, the machine learning model is configured togenerate a label (or a confidence score corresponding to the label)identifying the temporal distribution as a sick day. As another example,the machine learning model may be trained with a set of trainingexamples corresponding to a second event condition (e.g., a vacation)using a plurality sample temporal distributions in series correspondingto the second event condition (e.g., the vacation). Each sample temporaldistribution may be tagged or labeled with the respective eventcondition (e.g., a vacation label). Continuing this example, where aperson is taking a vacation, the overall number of electronic activityengaged in by the person may decrease to below a threshold for a seriesof days in a row (e.g., a week, for instance). The machine learningmodel may be trained with the series of temporal distributionscorresponding to the vacation such that, where a series of temporaldistributions of a user corresponding to a vacation is provided as aninput to the machine learning model, the machine learning model isconfigured to generate a label (or a confidence score corresponding tothe label) identifying each of the temporal distributions as vacationtime.

As described in greater detail above, the temporal distributiongeneration engine may be configured to generate various temporaldistributions of electronic activity for various time intervals. In someembodiments, the temporal distribution generation engine may generate atemporal distribution for a set of electronic activities engaged in bythe user after generation of the electronic activity pattern (e.g., at asubsequent point of time). The temporal distribution generation enginemay generate the temporal distribution for each electronic activityengaged in by the user during a subsequent time interval (referred toherein as a subsequent temporal distribution). The event conditiondetection engine may be configured to apply the event conditiondetection policy to the subsequent temporal distribution to determinewhether the subsequent temporal distribution satisfies the eventcondition detection policy for identifying an event condition.

In some embodiments, the event condition detection engine may beconfigured to identify, determine, or otherwise detect the eventcondition corresponding to a temporal distribution based on one or morecharacteristics of the subsequent temporal distribution satisfying acorresponding characteristic associated with the respective eventcondition. The event condition detection engine may maintain, include,or otherwise access a plurality of characteristics associated respectivelabels for corresponding event conditions. For instance, acharacteristic associated with an event condition for a sick day mayindicate a drop in electronic activity engaged in by the user for thetime interval below a standard deviation (or other threshold) from anaverage electronic activity engaged in by the user. The event conditiondetection engine may access a characteristic associated with the eventcondition (e.g., sick day). The event condition detection engine maydetermine whether the subsequent temporal distribution of the usersatisfies the characteristic (e.g., the average electronic activitiesengaged in by the user in the subsequent temporal distribution isoutside of a standard deviation from the average electronic activityengaged in by the user). The event condition detection engine may detectthe event condition (e.g., the sick day) based on the temporaldistribution satisfying the characteristic. As another example, wherethe user takes a vacation day, a temporal distribution for a businessday may include less than threshold number of electronic activitiesengaged in by the user (e.g., zero or close to zero electronicactivities). The event condition detection engine may store, include,maintain, or otherwise access a characteristic associated with thecondition corresponding to a vacation day (e.g., average electronicactivities or number of electronic activities engaged in by a given userfalling below a threshold). The event condition detection engine may beconfigured to detect an event condition corresponding to a vacation daybased on the subsequent temporal distribution satisfying thecorresponding characteristic associated with the vacation day eventcondition.

The event condition detection engine may be configured to store anassociation between the node profile 2804 of the user and the eventcondition. The event condition detection engine may be configured tostore the association between the node profile 2804 and event conditionto more accurately reflect the user's schedule. The event conditiondetection engine may be configured to store the association between thenode profile 2804 and event condition and in association with the regionof the time interval corresponding to the users work schedule. In someembodiments, the system 200 may be configured to generate a reportcorresponding to a schedule of the user. The report may be a schedule ofthe user across the time period. For instance, the system 200 may beconfigured to generate the report to reflect each time interval in whichthe user worked, and reflect the event conditions. The system 200 may beconfigured to generate a weekly report, a monthly report, etc. Thesystem 200 may be configured to communicate the report to the user, to amanager, leader, or other employee managing the user, and so forth.

Referring now to FIG. 30, illustrated is a flow diagram of a method 3000of inferring a time zone of a node profile using electronic activities.The method 3000 can be implemented or performed using any of thecomponents described above in conjunction with FIGS. 1-29 (e.g., thenode graph generation system 200) or the server system 3100 detailedbelow in conjunction with FIG. 31. The method 3000 may include accessingelectronic activities (3002). The method 3000 may include identifying aset of electronic activities for a node profile (3004). The method 3000may include identifying a timestamp for electronic activities in the set(3006). The method 3000 may include generating a temporal distributionof electronic activities (3008). The method 3000 may include determininga time zone (3010). The method 3000 may include storing an associationbetween the time zone and the node profile (3012).

The method 3000 may include accessing electronic activities (3002). Insome embodiments, the system 200 may access a plurality of electronicactivities transmitted or received by a plurality of participantscorresponding to one or more respective electronic accounts associatedwith a plurality of data source providers. The system 200 may access theelectronic activities as they are generated, sent, received, exchanged,or otherwise engaged in by the participants. In some embodiments, thesystem 200 may access the electronic activities when they are ingestedinto the system 200 from the data source provider(s). Each of theelectronic activities may include metadata corresponding to,identifying, or otherwise used for identifying participants of theelectronic activity. For instance, the metadata may include asender/initiator, recipient, other participants, etc. of the electronicactivity. Each of the electronic activities may include a timestamp. Thetimestamp may indicate the time in which the electronic activity wasgenerated, sent, received, or otherwise engaged in by theparticipant(s).

The method 3000 may include identifying a set of electronic activitiesfor a node profile (3004). In some embodiments, the system 200 mayidentify a set of electronic activities for a node profile engaged in bya participant of the plurality of participants linked to the nodeprofile within a time period. Each node profile may include, forinstance, a value for a field-value pair corresponding to an emailaddress, a value for a field-value pair corresponding to a phone number,etc. The system 200 may identify the set of electronic activities forthe node profile based on which electronic activities include a matchingvalue from the metadata in the node profile. As described above, themetadata may include a sender, recipient(s), etc. The system 200 maycross-reference the values from the metadata with data from the nodeprofiles to identify the set of electronic activities for the nodeprofile. The system 200 may identify the set of electronic activitieswithin a time period. The time period may be, for instance, a 30-daywindow, a 90-day window, a 180-day window, and so forth.

In some embodiments, the system 200 may determine a type of eachelectronic activity engaged in by a participant linked to the nodeprofile within a time period. The system 200 may select the electronicactivities for the set having a specific type or types, and may ignoreor discard electronic activities having other types. For example, thesystem 200 may select activities having a type corresponding toelectronic calendar events, but may discard electronic activitiescorresponding to text messages. The system 200 may determine the type ofeach electronic activity engaged in by determining which value for thefield-value pair of the node profile was used for associating theelectronic activity to the node profile. The system may correlatespecific fields of a node profile with specific types of electronicactivity (e.g., field corresponding to email address with an emailelectronic activity type, field corresponding to work phone number witha phone call electronic activity type, and so forth). The system 200 mayidentify the electronic activity type for each electronic activityengaged in by the user within the time period. The system 200 mayinclude, in the set of electronic activity, a subset of type(s) ofelectronic activity. For instance, in some embodiments, the system 200may limit the set to include only emails and phone calls, as suchelectronic activity may be more typical of a schedule for a user. Insome implementations, the system 200 may limit the set to includeelectronic calendar events, as such events may be more indicative of auser's time zone than other events. For example, a user may be morelikely to participate in other types of electronic activities, such asemails, outside of the users normal work hours, and therefore suchelectronic activities may be less useful for determining the user's timezone. On the other hand, a user may be less likely to attendappointments outside of the user's normal work hours (e.g., during themiddle of the night), and therefore such electronic activities may bemore useful for inferring the user's time zone.

The method 3000 may include identifying a timestamp for electronicactivities in the set (3006). In some embodiments, the system 200 mayidentify a timestamp at which each electronic activity was sent orreceived in the set of electronic activities (e.g., identified at 3004).The system 200 may parse the metadata of each electronic activity in theset to identify the timestamp for each electronic activity in the set.The timestamp may be a time in which the electronic activity was sent,received, or otherwise engaged in by the user. The system 200 may sorteach of the electronic activities by their respective timestamps. Hence,each electronic activity may be sorted or otherwise arranged inchronological order.

The method 3000 may include generating a temporal distribution ofelectronic activities (3008). In some embodiments, the system 200 maygenerate a temporal distribution of electronic activity for each timeinterval within the time period based on respective timestamps of eachelectronic activity of the set of electronic activities within the timeinterval. The system 200 may separate each electronic activity withinthe set into subsets of the electronic activities. Each time intervalmay correspond to, for instance, one day of the time period, one week ofthe time period, etc. The system 200 may maintain, for each time period,a set of time intervals. The system 200 may generate the temporaldistribution responsive to identifying the electronic activities in theset (e.g., at 3006). The system 200 may generate the temporaldistribution for each time interval of the time period. The system 200may generate the temporal distribution in real-time. The system 200 maygenerate the temporal distribution at the end of each time interval. Thesystem may generate the temporal distribution of electronic activitiesby compiling, for each time frame (e.g., 15 minute, half hour, hour,etc.) within the time interval, the number of electronic activitiesengaged in during the respective time frame. Hence, the temporaldistribution may reflect the number of electronic activities engaged inby the user across the time interval.

In some implementations, the method 3000 may include determining anelectronic activity pattern. In some embodiments, the system 200 maydetermine an electronic activity pattern based on the temporaldistribution of electronic activity for each time interval within thetime period. The system 200 may determine the electronic activitypattern following generation of each temporal distribution (e.g., ateach iteration of 3008). The system 200 may determine the electronicactivity pattern following generation of a temporal distribution foreach time interval within the time period. The system 200 may generatethe electronic activity pattern based on each of the temporaldistributions (e.g., generated at 3008) that fall within the timeperiod. The system 200 may determine the electronic activity pattern byapplying a pattern determination policy to each of the temporaldistributions within the time period. The pattern determination policymay be a rule-based policy, a probabilistic model, etc. The patterndetermination engine may be configured to apply the patterndetermination policy to each of the temporal distributions within thetime period for generating the electronic activity pattern for the user.The pattern determination policy may compute an average, mean, or otherstatistic of electronic activity for each time frame within a respectivetime interval in the time period. In some embodiments, the patterndetermination policy may filter one or more data outliers in thetemporal distribution (e.g., a number of electronic activities exceedinga standard deviation from the average or mean of electronic activitywithin a respective time frame). In some embodiments, the patterndetermination policy may be used by the pattern determination engine tofilter electronic activities within a respective time frame having amean or average less than one electronic activity.

The method 3000 may include determining a time zone (3010). In someimplementations, the system 200 may determine the time zone based on oneor more temporal distributions or electronic activity patterns of thenode profile. For example, the system 200 may compare at least onetemporal distribution for a node profile to one or more other temporaldistributions stored in a database or data structure and associated witha respective time zone. The database may include electronic activitytemporal distributions corresponding to each of a plurality of timezones (e.g., 24 temporal distributions each corresponding to arespective time zone). In some implementations, the stored temporaldistributions may be referred to as temporal distribution templates. Thesystem 200 may identify the temporal distribution template included inthe database that most closely matches at least one temporaldistribution of the user. For example, the system 200 may identify whichtemporal distribution template of the database most closely matches thetemporal distribution of the user based on which temporal distributiontemplate tracks the temporal distribution of the user, which temporaldistribution template has or includes upticks and downticks inelectronic activity within the same time frame as the temporaldistribution of the user, and so forth. When a match is determined, thesystem 200 may infer that the time zone for the user is the same as thetime zone of the matching temporal distribution template, based on thematch.

In some implementations, the temporal distribution templates can begenerated or selected based on electronic activities associated withnode profiles having known time zone information. For example, togenerate a temporal distribution template for a given time zone, thesystem 200 may identify at least one node profile that is known to beassociated with a user in that time zone. The system 200 may then accessa set of electronic activities for the identified node profile todetermine at least one temporal distribution of electronic activitiesfor the node profile, in a manner similar to that described above, andthe at least one temporal distribution may be stored as a temporaldistribution template for that time zone. In some implementations, thesystem 200 may store more than one temporal distribution template for agiven time zone. For example, the system 200 may store a respectivetemporal distribution template for each of a plurality of differentindustries, job titles, or any other characteristics of node profiles.In some implementations, typical electronic activity patterns may varyacross industries or job titles, even within the same time zone. Thus,in order to infer a time zone of a user based on the users electronicactivities, it may be useful to compare the users temporal distributionof electronic activities to a corresponding temporal distributiontemplate representing one or more node profiles of users in the sameindustry or with the same job title. To achieve this, the system 200 maygenerate and store temporal distribution templates for different jobtitles, industries, or any other criteria within a given time zone. Toinfer a time zone for a particular node profile, the system 200 mayselect one or more temporal distribution templates to compare with thetemporal distribution of the node profile based on match between anindustry or job title of the node profile and a corresponding industryor job title of the one or more temporal distribution templates.

In some implementations, additional information can be used to furtherconfirm or corroborate the user's inferred time zone. For example, themethod 3000 can include parsing at least one electronic activity of theset of electronic activities to identify a character stringcorresponding to a geographic location. The geographic location can beor can include a residential address, a business address, a city, astate, a country, a zip code, or the like. The geographic location canbe extracted, for example, from the body of an electronic activity. Insome implementations, the location can be extracted from a signatureblock of an electronic activity corresponding to an email. The time zonecan be determined based in part on the geographic location. For example,the geographic location can be matched to a corresponding time zone thatthe geographic location is positioned in. Thus, the system 200 canincrease or decrease a confidence level associated with the inferredtime zone, based on the geographic location extracted from theelectronic activity.

The method 3000 may include storing an association between the time andthe node profile (3012). In some embodiments, the system 200 may storean association between the time zone and the node profile in one or moredata structures. The one or more data structures can be maintained bythe data processing system 9300 or the system 200. The system 200 storean association between the node profile of the user and the identifiedtime zone of the user. The system 200 may store the association in adatabase, memory, server, or other data structure. In some embodiments,the time zone may be static (e.g., once the time zone is identified, itremains the same). In some embodiments, the identified region may bedynamic (e.g., the time zone may be changed over time). For example, insome embodiments, the time zone for the node profile may be updated atvarious points in time. The system 200 may update the time zone for thenode profile on a periodic basis (e.g., once every week, once everymonth, once every six months, once every year, etc.). The system 200 mayupdate the time zone in a manner similar to that described above inconnection with steps 3002-3010, for example to determine whether orwhen a person's time zone changes.

In some implementations, a change in time zone may correspond to anevent such as travel or vacation for a user associated with the nodeprofile. For example, the system 200 may detect an event condition. Thesystem 200 may detect an event condition by identifying a second set ofelectronic activities engaged in by the participant corresponding to thenode profile within a second time period (e.g., similar to 3004). Thesystem 200 may generate a temporal distribution of electronic activityfor the time interval in the second time period (e.g., similar to 3008).The system 200 may compare the temporal distribution to temporaldistributions of the user for other time periods (or to an electronicactivity pattern for the user based on multiple time periods) todetermine that the temporal distribution satisfies an event conditiondetection policy for detecting an event condition. The system 200 maystore an association between the node profile and the event condition inone or more data structures.

In some embodiments, the event condition detection policy can be used bythe system to detect the event condition from a plurality of eventconditions based on the determined electronic activity patternsatisfying a corresponding characteristic of a plurality ofcharacteristics associated with a respective event condition. The eventcondition detection engine may access a plurality of characteristicsassociated respective labels for corresponding event conditions. Forinstance, when the user takes a vacation, a temporal distribution for avacation period may include less than a threshold number of electronicactivities engaged in by the user (e.g., zero or close to zeroelectronic activities). The event condition detection engine may accessa characteristic associated with the condition corresponding to avacation day (e.g., average electronic activities or number ofelectronic activities engaged in by a given user falling below athreshold). The event condition detection engine may detect an eventcondition corresponding to a vacation day based on the temporaldistribution satisfying the corresponding characteristic associated withthe vacation day event condition. In some implementations, the totalnumber of the user's electronic activities may not substantiallyincrease or decrease, but may instead be time shifted relative to theuser's electronic activities during a different time period. Forexample, this may indicate that the user has traveled to a differenttime zone.

In some embodiments, the event condition detection policy includes amachine learning model trained to classify temporal distributions ofelectronic activity. The system 200 may use the machine learning modelto determine that the temporal distribution satisfies the eventcondition detection policy. In some embodiments, the machine learningmodel may be trained to identify several event condition types. Themachine learning model may use a first set of training examplesincluding temporal distributions corresponding to a first eventcondition type and a first label identifying the first event conditiontype. The machine learning model may use a second set of trainingexamples including temporal distributions corresponding to a secondevent condition type and a second label identifying the first eventcondition type. The system 200 may train the machine learning modelusing the first and second set of training examples. Following training,the machine learning model may detect the first and second eventconditions based on traits, characteristics, and so forth of thetemporal distributions of the user for a time interval. Continuing theexamples described above, the machine learning model may be trained todetect event conditions corresponding to sick days and vacation days.For instance, the machine learning model may be trained with a set oftraining examples corresponding to a first event condition (e.g., travelto a new time zone) using a plurality sample temporal distributionscorresponding to the first event condition (e.g., the new time zone).Each sample temporal distribution may be tagged or labeled with therespective event condition. Continuing this example, where a person istaking a vacation and traveling to a new time zone, the overall numberof electronic activity engaged in by the person may decrease and/or theelectronic activity temporal distribution may be time shifted relativeto the person's typical time zone. The machine learning model may betrained with temporal distributions corresponding to the travel suchthat, where a temporal distribution corresponding to period of travel toa new time zone is provided as an input to the machine learning model,the machine learning model is configured to generate a label (or aconfidence score corresponding to the label) identifying the temporaldistribution as travel to a new time zone. In some implementations, themachine learning model may further identify a purpose for the travel(e.g., whether the travel is for vacation or for work).

The event condition detection engine may store an association betweenthe node profile of the user and the event condition. The eventcondition detection engine may store the association between the nodeprofile and event condition to more accurately reflect the user'slocation or current time zone. The event condition detection engine maystore the association between the node profile 2804 and event conditionand in association with the region of the time interval corresponding tothe user's work schedule.

In some implementations, the method 3000 can include generating, by thesystem 200, a notification for a user associated with the node profile.For example, the notification can be any message or alert to becommunicated to the user, such as an instruction or recommendation toperform a certain action (e.g., a work-related task). In someimplementations, the system 200 can schedule transmission of thenotification to a computing device of the user associated with the nodeprofile based on the determined time zone. For example, it may bedesirable to provide the notification to the user at a time (or during awindow of time) when the user is likely to be able to receive, view, oract on the notification, such as during daytime hours or during typicalwork hours in the users time zone. If the notification is instead sentduring different hours (e.g., during the night), the user may be lesslikely to receive, view, or act on the notification. Thus, in someimplementations, the system 200 can be configured to identify one ormore restricted notification periods based on the determined time zone.The restricted notification periods may be any periods that aredetermined to be periods during which the user is unlikely to receive,view, or act on a notification, such as during the early morning ornight time hours in the user's time zone. In some the system 200 canrestrict transmission of the notification to the computing device of theuser associated with the node profile during the restricted notificationperiod.

21. Computer System

Various operations described herein can be implemented on computersystems, which can be of generally conventional design. FIG. 31 shows asimplified block diagram of a representative server system 3100 andclient computer system 3114 usable to implement certain embodiments ofthe present disclosure. In various embodiments, server system 3100 orsimilar systems can implement services or servers described herein orportions thereof. Client computer system 3114 or similar systems canimplement clients described herein. Each of the systems 9300, 200 andothers described herein can be similar to the server system 3100.

Server system 3100 can have a modular design that incorporates a numberof modules 3102 (e.g., blades in a blade server embodiment); while twomodules 3102 are shown, any number can be provided. Each module 3102 caninclude processing unit(s) 3104 and local storage 3106.

Processing unit(s) 3104 can include a single processor, which can haveone or more cores, or multiple processors. In some embodiments,processing unit(s) 3104 can include a general-purpose primary processoras well as one or more special-purpose co-processors such as graphicsprocessors, digital signal processors, or the like. In some embodiments,some or all processing units 3104 can be implemented using customizedcircuits, such as application specific integrated circuits (ASICs) orfield programmable gate arrays (FPGAs). In some embodiments, suchintegrated circuits execute instructions that are stored on the circuititself. In other embodiments, processing unit(s) 3104 can executeinstructions stored in local storage 3106. Any type of processors in anycombination can be included in processing unit(s) 3104.

Local storage 3106 can include volatile storage media (e.g.,conventional DRAM, SRAM, SDRAM, or the like) and/or non-volatile storagemedia (e.g., magnetic or optical disk, flash memory, or the like).Storage media incorporated in local storage 3106 can be fixed, removableor upgradeable as desired. Local storage 3106 can be physically orlogically divided into various subunits such as a system memory, aread-only memory (ROM), and a permanent storage device. The systemmemory can be a read-and-write memory device or a volatileread-and-write memory, such as dynamic random-access memory. The systemmemory can store some or all of the instructions and data thatprocessing unit(s) 3104 need at runtime. The ROM can store static dataand instructions that are needed by processing unit(s) 3104. Thepermanent storage device can be a non-volatile read-and-write memorydevice that can store instructions and data even when module 3102 ispowered down. The term “storage medium” as used herein includes anymedium in which data can be stored indefinitely (subject to overwriting,electrical disturbance, power loss, or the like) and does not includecarrier waves and transitory electronic signals propagating wirelesslyor over wired connections.

In some embodiments, local storage 3106 can store one or more softwareprograms to be executed by processing unit(s) 3104, such as an operatingsystem and/or programs implementing various server functions such asfunctions of the data processing system 9300 of FIG. 2, the node graphgeneration system 300, or any other system described herein, or anyother server(s) associated with data processing system 9300 of FIG. 2 orthe node graph generation system 300 or any other system describedherein.

“Software” refers generally to sequences of instructions that, whenexecuted by processing unit(s) 3104 cause server system 3100 (orportions thereof) to perform various operations, thus defining one ormore specific machine embodiments that execute and perform theoperations of the software programs. The instructions can be stored asfirmware residing in read-only memory and/or program code stored innon-volatile storage media that can be read into volatile working memoryfor execution by processing unit(s) 3104. Software can be implemented asa single program or a collection of separate programs or program modulesthat interact as desired. From local storage 3106 (or non-local storagedescribed below), processing unit(s) 3104 can retrieve programinstructions to execute and data to process in order to execute variousoperations described above.

In some server systems 3100, multiple modules 3102 can be interconnectedvia a bus or other interconnect 3108, forming a local area network thatsupports communication between modules 3102 and other components ofserver system 3100. Interconnect 3108 can be implemented using varioustechnologies including server racks, hubs, routers, etc.

A wide area network (WAN) interface 3110 can provide data communicationcapability between the local area network (interconnect 3108) and alarger network, such as the Internet. Conventional or other activitiestechnologies can be used, including wired (e.g., Ethernet, IEEE 802.3standards) and/or wireless technologies (e.g., Wi-Fi, IEEE 802.11standards).

In some embodiments, local storage 3106 is intended to provide workingmemory for processing unit(s) 3104, providing fast access to programsand/or data to be processed while reducing traffic on interconnect 3108.Storage for larger quantities of data can be provided on the local areanetwork by one or more mass storage subsystems 3112 that can beconnected to interconnect 3108. Mass storage subsystem 3112 can be basedon magnetic, optical, semiconductor, or other data storage media. Directattached storage, storage area networks, network-attached storage, andthe like can be used. Any data stores or other collections of datadescribed herein as being produced, consumed, or maintained by a serviceor server can be stored in mass storage subsystem 3112. In someembodiments, additional data storage resources may be accessible via WANinterface 3110 (potentially with increased latency).

Server system 3100 can operate in response to requests received via WANinterface 3110. For example, one of modules 3102 can implement asupervisory function and assign discrete tasks to other modules 3102 inresponse to received requests. Conventional work allocation techniquescan be used. As requests are processed, results can be returned to therequester via WAN interface 3110. Such operation can generally beautomated. Further, in some embodiments, WAN interface 3110 can connectmultiple server systems 3100 to each other, providing scalable systemscapable of managing high volumes of activity. Conventional or othertechniques for managing server systems and server farms (collections ofserver systems that cooperate) can be used, including dynamic resourceallocation and reallocation.

Server system 3100 can interact with various user-owned or user-operateddevices via a wide-area network such as the Internet. An example of auser-operated device is shown in FIG. 31 as client computing system3114. Client computing system 3114 can be implemented, for example, as aconsumer device such as a smartphone, other mobile phone, tabletcomputer, wearable computing device (e.g., smart watch, eyeglasses),desktop computer, laptop computer, and so on.

For example, client computing system 3114 can communicate via WANinterface 3110. Client computing system 3114 can include conventionalcomputer components such as processing unit(s) 3116, storage device3118, network interface 3120, user input device 3122, and user outputdevice 3124. Client computing system 3114 can be a computing deviceimplemented in a variety of form factors, such as a desktop computer,laptop computer, tablet computer, smartphone, other mobile computingdevice, wearable computing device, or the like.

Processor 3116 and storage device 3118 can be similar to processingunit(s) 3104 and local storage 3106 described above. Suitable devicescan be selected based on the demands to be placed on client computingsystem 3114; for example, client computing system 3114 can beimplemented as a “thin” client with limited processing capability or asa high-powered computing device. Client computing system 3114 can beprovisioned with program code executable by processing unit(s) 3116 toenable various interactions with server system 3100 of a messagemanagement service such as accessing messages, performing actions onmessages, and other interactions described above. Some client computingsystems 3114 can also interact with a messaging service independently ofthe message management service.

Network interface 3120 can provide a connection to a wide area network(e.g., the Internet) to which WAN interface 3110 of server system 3100is also connected. In various embodiments, network interface 3120 caninclude a wired interface (e.g., Ethernet) and/or a wireless interfaceimplementing various RF data communication standards such as Wi-Fi,Bluetooth, or cellular data network standards (e.g., 3G, 4G, LTE, etc.).

User input device 3122 can include any device (or devices) via which auser can provide signals to client computing system 3114; clientcomputing system 3114 can interpret the signals as indicative ofparticular user requests or information. In various embodiments, userinput device 3122 can include any or all of a keyboard, touch pad, touchscreen, mouse or other pointing device, scroll wheel, click wheel, dial,button, switch, keypad, microphone, and so on.

User output device 3124 can include any device via which clientcomputing system 3114 can provide information to a user. For example,user output device 3124 can include a display to display imagesgenerated by or delivered to client computing system 3114. The displaycan incorporate various image generation technologies, e.g., a liquidcrystal display (LCD), light-emitting diode (LED) including organiclight-emitting diodes (OLED), projection system, cathode ray tube (CRT),or the like, together with supporting electronics (e.g.,digital-to-analog or analog-to-digital converters, signal processors, orthe like). Some embodiments can include a device such as a touchscreenthat function as both input and output device. In some embodiments,other user output devices 3124 can be provided in addition to or insteadof a display. Examples include indicator lights, speakers, tactile“display” devices, printers, and so on.

Some embodiments include electronic components, such as microprocessors,storage and memory that store computer program instructions in acomputer readable storage medium. Many of the features described in thisspecification can be implemented as processes that are specified as aset of program instructions encoded on a computer readable storagemedium. When these program instructions are executed by one or moreprocessing units, they cause the processing unit(s) to perform variousoperation indicated in the program instructions. Examples of programinstructions or computer code include machine code, such as is producedby a compiler, and files including higher-level code that are executedby a computer, an electronic component, or a microprocessor using aninterpreter. Through suitable programming, processing unit(s) 3104 and3116 can provide various functionality for server system 3100 and clientcomputing system 3114, including any of the functionality describedherein as being performed by a server or client, or other functionalityassociated with message management services.

It will be appreciated that server system 3100 and client computingsystem 3114 are illustrative and that variations and modifications arepossible. Computer systems used in connection with embodiments of thepresent disclosure can have other capabilities not specificallydescribed here. Further, while server system 3100 and client computingsystem 3114 are described with reference to particular blocks, it is tobe understood that these blocks are defined for convenience ofdescription and are not intended to imply a particular physicalarrangement of component parts. For instance, different blocks can bebut need not be located in the same facility, in the same server rack,or on the same motherboard. Further, the blocks need not correspond tophysically distinct components. Blocks can be configured to performvarious operations, e.g., by programming a processor or providingappropriate control circuitry, and various blocks might or might not bereconfigurable depending on how the initial configuration is obtained.Embodiments of the present disclosure can be realized in a variety ofapparatus including electronic devices implemented using any combinationof circuitry and software.

While the disclosure has been described with respect to specificembodiments, one skilled in the art will recognize that numerousmodifications are possible. For instance, although specific examples ofrules (including triggering conditions and/or resulting actions) andprocesses for generating suggested rules are described, other rules andprocesses can be implemented. Embodiments of the disclosure can berealized using a variety of computer systems and communicationtechnologies including but not limited to specific examples describedherein.

Embodiments of the present disclosure can be realized using anycombination of dedicated components and/or programmable processorsand/or other programmable devices. The various processes describedherein can be implemented on the same processor or different processorsin any combination. Where components are described as being configuredto perform certain operations, such configuration can be accomplished,e.g., by designing electronic circuits to perform the operation, byprogramming programmable electronic circuits (such as microprocessors)to perform the operation, or any combination thereof. Further, while theembodiments described above may make reference to specific hardware andsoftware components, those skilled in the art will appreciate thatdifferent combinations of hardware and/or software components may alsobe used and that particular operations described as being implemented inhardware might also be implemented in software or vice versa.

Computer programs incorporating various features of the presentdisclosure may be encoded and stored on various computer readablestorage media; suitable media include magnetic disk or tape, opticalstorage media such as compact disk (CD) or DVD (digital versatile disk),flash memory, and other non-transitory media. Computer readable mediaencoded with the program code may be packaged with a compatibleelectronic device, or the program code may be provided separately fromelectronic devices (e.g., via Internet download or as a separatelypackaged computer-readable storage medium).

Thus, although the disclosure has been described with respect tospecific embodiments, it will be appreciated that the disclosure isintended to cover all modifications and equivalents within the scope ofthe following claims.

What is claimed is:
 1. A method comprising: accessing, by one or moreprocessors, a plurality of electronic activities transmitted or receivedvia a plurality of electronic accounts associated with a data sourceprovider; identifying, by the one or more processors, for a node profilemaintained by the one or more processors, a set of electronic activitiessent from or received by an electronic account of the plurality ofelectronic accounts linked to the node profile within a time period;identifying, by the one or more processors, for each electronic activityof the set of electronic activities, a timestamp at which the electronicactivity was sent or received; generating, by the one or moreprocessors, for each of a plurality of time intervals within the timeperiod, a temporal distribution of electronic activity for the timeinterval based on respective timestamps of each electronic activity ofthe set of electronic activities within the time interval; determining,by the one or more processors, a time zone of the node profile based onthe temporal distribution of electronic activity; and storing, by theone or more processors in one or more data structures, an associationbetween the determined time zone and the node profile.
 2. The method ofclaim 1, further comprising: receiving, by the one or more processors,an electronic activity not included in the set of electronic activities;parsing, by the one or more processors, the electronic activity toidentify a participant and a timestamp; determining, by the one or moreprocessors, a plurality of candidate node profiles, based on theparticipant; and selecting, by the one or more processors, one of theplurality of candidate node profiles to match with the electronicactivity based on a time zone of selected candidate node profile and thetimestamp of the electronic activity.
 3. The method of claim 1, whereinthe temporal distribution is a first temporal distribution and whereindetermining the time zone of the node profile further comprises:identifying, by the one or more processors, a plurality of secondtemporal distributions each associated with a respective predeterminedtime zone; selecting, by the one or more processors, one of theplurality of second temporal distributions based on a comparison withthe first temporal distribution; and determining, by the one or moreprocessors, the time zone based on the respective predetermined timezone of the selected second temporal distribution.
 4. The method ofclaim 3, wherein each second temporal distribution is generated based ona respective second set of electronic activities of one or more secondnode profiles, each second node profile associated with the respectivepredetermined time zone.
 5. The method of claim 1, further comprising:identifying, by the one or more processors, a system of record having atleast one record object associated with the node profile; andtransmitting, by the one or more processors, an instruction to cause thesystem of record to update a time zone field-value pair of the at leastone record object corresponding to the node profile.
 6. The method ofclaim 1, further comprising updating, by the one or more processors, atime zone field-value pair of the node profile to match the determinedtime zone.
 7. The method of claim 1, further comprising: generating, bythe one or more processors, a notification for a user associated withthe node profile; and scheduling, by the one or more processors,transmission of the notification to a computing device of the userassociated with the node profile based on the determined time zone. 8.The method of claim 7, wherein scheduling transmission of thenotification further comprises: identifying, by the one or moreprocessors, a restricted notification period based on the determinedtime zone; and restricting, by the one or more processors, transmissionof the notification to the computing device of the user associated withthe node profile during the restricted notification period.
 9. Themethod of claim 1, further comprising: parsing, by the one or moreprocessors, a first electronic activity of the set of electronicactivities to identify a character string corresponding to a geographiclocation; and determining, by the one or more processors, the time zonebased on the geographic location.
 10. The method of claim 9, whereinparsing the first electronic activity of the set of electronicactivities to identify the character string corresponding to thegeographic location further comprises extracting, by the one or moreprocessors, the geographic location from a body of the first electronicactivity.
 11. The method of claim 1, wherein identifying the set ofelectronic activities comprises selecting each electronic activity toinclude in the set of electronic activities responsive to determiningthat the electronic activity is of a first electronic activity type. 12.The method of claim 1, further comprising: comparing, by the one or moreprocessors, the time zone with a value of a field-value pair of the nodeprofile corresponding to a location; and updating, by the one or moreprocessors, a confidence score of the value of the field-value pair ofthe node profile corresponding to the location, based on the comparison.13. The method of claim 1, wherein determining the time zone of the nodeprofile based on the temporal distribution of electronic activitycomprises determining, by the one or more processors, that the temporaldistribution of electronic activity corresponds to the time zone of thenode profile using a machine learning model trained to classify temporaldistributions of electronic activity.
 14. A system comprising: one ormore hardware processors configured by machine-readable instructions to:access a plurality of electronic activities transmitted or received viaa plurality of electronic accounts associated with a data sourceprovider; identify, for a node profile maintained by the one or moreprocessors, a set of electronic activities sent from or received by anelectronic account of the plurality of electronic accounts linked to thenode profile within a time period; identify, for each electronicactivity of the set of electronic activities, a timestamp at which theelectronic activity was sent or received; generate, for each of aplurality of time intervals within the time period, a temporaldistribution of electronic activity for the time interval based onrespective timestamps of each electronic activity of the set ofelectronic activities within the time interval; determine a time zone ofthe node profile based on the temporal distribution of electronicactivity; and store, in one or more data structures, an associationbetween the determined time zone and the node profile.
 15. The system ofclaim 14, wherein the one or more hardware processors are furtherconfigured by the machine readable instructions to: receive anelectronic activity not included in the set of electronic activities;parse the electronic activity to identify a participant and a timestamp;determine a plurality of candidate node profiles, based on theparticipant; and select one of the plurality of candidate node profilesto match with the electronic activity based on a time zone of selectedcandidate node profile and the timestamp of the electronic activity. 16.The system of claim 14, wherein the temporal distribution is a firsttemporal distribution and wherein the one or more hardware processorsare further configured by the machine readable instructions to determinethe time zone of the node profile by: identifying a plurality of secondtemporal distributions each associated with a respective predeterminedtime zone; selecting one of the plurality of second temporaldistributions based on a comparison with the first temporaldistribution; and determining the time zone based on the respectivepredetermined time zone of the selected second temporal distribution.17. The system of claim 16, wherein the one or more hardware processorsare further configured by the machine readable instructions to generateeach second temporal distribution based on a respective second set ofelectronic activities of one or more second node profiles, each secondnode profile associated with the respective predetermined time zone. 18.The system of claim 14, wherein the one or more hardware processors arefurther configured by the machine readable instructions to: identify asystem of record having at least one record object associated with thenode profile; and transmit an instruction to cause the system of recordto update a time zone field-value pair of the at least one record objectcorresponding to the node profile.
 19. The system of claim 14, whereinthe one or more hardware processors are further configured by themachine readable instructions to update a time zone field-value pair ofthe node profile to match the determined time zone.
 20. A non-transientcomputer-readable storage medium having instructions embodied thereon,the instructions being executable by one or more processors to perform amethod comprising: accessing a plurality of electronic activitiestransmitted or received via a plurality of electronic accountsassociated with a data source provider; identifying, for a node profilemaintained by the one or more processors, a set of electronic activitiessent from or received by an electronic account of the plurality ofelectronic accounts linked to the node profile within a time period;identifying, for each electronic activity of the set of electronicactivities, a timestamp at which the electronic activity was sent orreceived; generating, for each of a plurality of time intervals withinthe time period, a temporal distribution of electronic activity for thetime interval based on respective timestamps of each electronic activityof the set of electronic activities within the time interval;determining a time zone of the node profile based on the temporaldistribution of electronic activity; and storing, in one or more datastructures, an association between the determined time zone and the nodeprofile.